Essay 1: Housing Factors Affecting Mobility: the Impact on Corporate Transfers

THREE ESSAYS ON SELLERS’ BEHAVIOR IN THE HOUSING MARKET

SVETOSLAVA ALEXANDROVA

Bachelor of Business Administration

Cleveland State University

May 2003

Master of Business Administration

Cleveland State University

May 2007

Submitted in partial fulfillment of requirements for the degree

DOCTOR OF BUSINESS ADMINISTRATION

at the

CLEVELAND STATE UNIVERSITY

MAY 2017

©COPYRIGHT BY SVETOSLAVA ALEXANDROVA 2016

We hereby approve this dissertation

for

Svetoslava Alexandrova

Candidate for the

Doctor of Business Administration degree for the

Department of Finance

And

CLEVELAND STATE UNIVERTSITY

College of Graduate Studies by

______

Committee Chair, Dr. Alan Reichert Department of Finance/October 6th, 2015

______

Committee Co-Chair, Dr. Haigang Zhou Department of Finance/October 6th, 2015

______

Prof. Dr. Dieter Gramlich Baden-Wuerttemberg Cooperative University/October 6th, 2016

______

Dr. Walter Rom Department of Operations and Supply Chain Management/October 6th, 2015

October 6th, 2015 ______Date of Defense

THREE ESSAYS ON SELLERS’ BEHAVIOR IN THE HOUSING MARKET

SVETOSLAVA ALEXANDROVA

ABSTRACT

Housing markets exhibit some puzzling behavior that cannot be completely explained by rational market dynamics. The neoclassical economic theory posits that rational sellers and rational buyers in the housing market will look at the current market price in order to determine a value of a property. Studies, however, show that physiological biases may affect the decision- making process of both sellers and buyers.

I examine the behavior of sellers in the housing market in three different settings. In essay

1, I analyze the effects of the health of the housing market on mobility. In Essay 2, I study the effects of sellers’ loss aversion on listing price and time on the market within the prospect theory framework. In Essay 3, I focus on identifying stress in the housing market by developing a stress index and commencing the design of an Early Warning System that incorporates signals from the market and behaviors from sellers to indicate increasing levels of pressure. I utilize a data set of private home sale transactions of corporate relocations for the period 2004-2014.

The results of the first study from the stepwise logit models on series of economic variables and demographic factors show that relocating employees facing negative equity situations and equity less than 5% of home value have a greater chance of rejecting relocation while

iv economic factors like affordability and credit availability have a positive effect on their ability to move. Essay 2 results indicate that a seller who faces a loss will set up an asking price 5.69 percent higher than they would otherwise. Additionally, sellers facing a loss will experience a reduction in the hazard rate of sale resulting in longer time on the market while income and family status have effect on loss aversion and time on market. In the last essay, I hypothesize that economic signals and home sellers’ behaviors can explain the variability of the housing market stress index proxied by a transformed S&P500/Case

Shiller Index. The preliminary results of the autoregressive models find that housing variables and market expectations of the “informed sellers” have statistically significant explanatory power.

ESSAY 1: HOUSING FACTORS AFFECTING MOBILITY: THE IMPACT ON CORPORATE TRANSFERS

HOUSING FACTORS AFFECTING MOBILITY: THE IMPACT ON CORPORATE TRANSFERS

SVETOSLAVA ALEXANDROVA

ABSTRACT

Using private home sale transactional data of U.S. domestic homeowners' relocation moves from 2004 to 2014, this study examines the effects of the housing market on mobility and more specifically corporate transfers. There are various factors that drive the willingness of an employee to accept a relocation transfer that can be categorized in three main groups - career, family and economic determinants. I hypothesize that the recent woes of the housing market have significantly affected the ability of relocating employees to accept corporate relocation offers to move to a new location. More specifically, the declining housing prices and the eroding house equity have increased the frequency of relocating homeowners facing negative equity and loss on sale situations. The increased burden of those financial constraints results in diminishing ability of employees to accept offers to relocate. This study contributes to the mobility research by directly examining the link between accepting or rejecting a corporate transfer and the ability of the relocating employee to dispose of his or her residence in the old location. The stepwise logit regression results show that relocating employees facing negative equity situations and equity less than 5% of home value have a greater chance of rejecting a relocation transfer decreasing their mobility. Housing market health and level of affordability also have a

vii significant effect on the ability of employees to relocate. Additionally, the homesale programs offered by the employer as part of the relocation package also play a role in the willingness of employees to relocate. Additionally, our results show that certain demographic factors such as gender and family status also impact the decision to move to a new location. Lastly, I examine three categories of destination factors and determine that education and certain types of crimes affect the decision to move.

viii

TABLE OF CONTENTS

PAGE

ABSTRACT………………………………………………………………………...vii

LIST OF TABLES…………………………………………………………………..xi

LIST OF FIGURES…………………………………………………………………xii

CHAPTER

I. INTRODUCTION………………………………………………………....1

II. MIGRATION AND MOBILITY………………………………………….6

III. CORPORATE RELOCATION OVERVIEW……………………………..9 3.1 Corporate Relocation Process………………………………………….9

3.2 Relocation HomeSale Process…………………………………...... 11

3.2.1 Overview of the Relocation HomeSale Process…………….....12

3.2.2 Home Marketing Process...……………………………………15

3.2.3 Relocation Appraisal.………………………………………….17

3.2.4 Tax Implication of HomeSale Process.………………………..18

IV. LITERATURE REVIEW.…………………………………………………20

V. MODEL AND HYPOTHESES.…………………………………………..23

5.1 Theoretical Model….………………………………………………….23

5.2 Hypotheses……………….…………………………………………....24

5.3 Methodology………………………………………………………….25

VI. EMPIRICAL MODELS AND VARIABLES…………………………….26

6.1 Factors Affecting the Decision to Relocate…………………………...26

6.1.1 Personal and Family Factors………………………………….26

6.1.2 Economic Factors……………………………………………..27

6.1.3 Compensation and Career Opportunities…………………..…28

6.1.4 Destination Location Factors…………………………………28

6.1.5 Housing Market Factors………………………………………28

6.2 Empirical Models and Variable Discussion…………………………..29

6.2.1 Empirical Models……………………………………………..29

6.2.2 Dependent Variable…………………………………………...33

6.2.3 Independent Variables………………………………………...33

VII. DATA AND SAMPLING PROCESS…………………………………….38

VIII. EMPIRICAL RESULTS…………………………………………………..46

8.1 Descriptive Statistics…………………………………………………..46

8.2 Empirical Results……………………………………………………...48

8.3 Destination factors impact on willingness to relocate………………...55

IX. CONCLUSION……………………………………………………………62

BIBLIOGRAPHY…………………………………………………………………64

APPENDIX………………………………………………………………………..67

A. Tax Concepts in Relocation – Eleven Key Elements and Procedures of an Amended Value

LIST OF TABLES

Table Page

Table I. Variable Legend ...... 31

Table II. Percentage of Rejected Transfers per State ...... 42

Table III. Sample Size and % of Independent Variables to Rejected and Accepted Transfers ...... 44

Table IV. Sample Size and % of Demographic Variables ...... 45

Table V. Summary Statistics...... 46

Table VI. Summary Statistics – Expanded Model ...... 46

Table VII. Correlation Matrix ...... 47

Table VIII. Empirical Results Model 1...... 48

Table IX. Empirical Results Model 2 ...... 50

Table X. Empirical Results Model 2- Robustness Check ...... 52

Table XI. Empirical Results Model 3 ...... 53

Table XII. Empirical Results Model 4 ...... 54

Table XIII. Summary Statistics - Model 5 …………..…………………………………..57

Table XIV. Correlation Model..…………..……………………………………………..59

Table XV. Empirical Results Model 5…………..……………………………………..59

LIST OF FIGURES

Figure Page

Figure 1. Reluctance to Relocate ...... 3

Figure 2. Types of HomeSale Transactions ...... 15

Figure 3. Reasons for Moving ...... 41

Figure 4. HomeSale Transactions by State ...... 42

Figure 5. Distribution of Corporate Transfers by Year...... 43

xii

CHAPTER I

INTRODUCTION

Employee mobility is an important topic for many reasons. From employers' perspective, mobility is an integral part of talent development by allowing employees to increase their skills and experience and by addressing skill and talent voids in emerging markets. From an employee perspective, corporate transfers to new destinations provide valuable opportunities for advancement and gaining global experience and skills.

While many relocations result in hierarchical moves, there are significant implications to employees' work and family associated with geographic relocation that require adjustments. Accepting a relocation transfer is a balancing act between the work and non-work aspects of the move. Many factors such as destination characteristics (Noe and Barber, (1993)) and family reasons (Allard, (1990)) affect the decision making process.

With that being said, the ability of an employee and family to relocate in the U.S. is also strongly related to their ability to sell their current primary residence and purchase a new one in the new destination. As a result, it is safe to assume that the busts and booms in the

U.S. housing market have a significant effect on residential mobility and more specifically relocation volume and the ability of an employee to relocate.

A recent research study of the Worldwide Employee Relocation Council (ERC)1 shows that the real estate crisis in the U.S. and the subsequent financial crisis have had a significant impact on the employees' mobility and their willingness to relocate. The survey results show that depressed housing market and negative equity situations are the top two reasons why employees may not accept relocation. In addition, employers cite that around

5 percent of employees offered relocation decide not to move. Figure 1 displays a summary of reasons obtained as part of the 2013 Worldwide ERC U.S. Transfer & Cost Survey.

Similar to the previous years, the main two reasons why employees are reluctant to accept a transfer is related to the real estate market. Facing negative equity situation and a slowed appreciation and depressed housing market are the top two reasons why transferees are less willing to move. If compared to previous years, the percentage of companies reporting depressed market as a reason to decline relocation has decreased from 90% in 2008 to 77% in 2013. The ERC survey concludes that the main reason behind the drop is improving consumer confidence and improving housing market conditions. However, it is still important to note that the willingness to move as well as the financial ability to do so is still tied to the lingering effects of 2007 financial and housing crisis as indicated in the survey data.

1 Worldwide Employee Relocation Council is a trade organization established in 1964. As a workforce mobility association ERC assists professionals who oversee, manage, or support U.S. domestic and international employee transfers/relocations

Figure 1. Reluctance to Relocate

The figure below shows the reasons for employee reluctance to relocate. The graph is based on 51 companies experiencing problems with relocating employees. Percentages do not total to 100% because of multiple responses. Source: 2013 Worldwide ERC U.S. Transfer Volume & Cost Survey

In summary, mobility is affected by various bio-demographic and economic factors. Overall, employees should have positive attitude towards relocation (Noe and

Barber,1993) but the survey data from 2013 as reported by ERC continues to show uneasiness on employees' behalf citing challenges of the housing market and tight credit controls to obtain a new mortgage as important reasons to decline a relocation offer. It is important to note that companies do not offer any assistance to relocation employees that face foreclosure. As presented by the 2013 ERC survey, around 92% of companies that have participated in the survey do not offer such assistance; however, around 22% offer some type of loss on sale or short sale assistance. This puts additional burden on relocating employees’ (transferees’) mobility and their ability to dispose of their current residence and accept a transfer to a new location.

This study examines the housing market factors' effects on mobility and more specifically, their impact on the ability of the employee to accept a corporate transfer.

Households that cannot sell are “locked- in” the old location. Rational response to falling

3 market prices is to hold on to the existing housing investments in anticipation of future returns (Case and Schiller, 1989). In addition, low and negative equity affects household by further restricting mobility and diminishing the ability to move (Henley, 1998; Ferreira et al., 2010). Since buyer-seller housing market efficiencies are affected by the volume and affordability of the homes for sale, they also play a role in mobility (Wheaton, 1990). The market efficiencies affect the sellers’ ability to sell their home by providing efficient price determination and thus have direct impact on mobility and relocation. This study contributes to the current literature by focusing on the existence of negative equity and housing market health as primary factors of the housing market effects on the decision to relocate. Previous studies have incorporated data from surveys while the data used in this study is unique to relocation and provides transactional level of home sale information such as listing price, days on market, equity, sales price etc. Each relocation offer is either accepted or rejected and the sale of the home is examined to determine the role it has played into the decision.

The results of the stepwise logistic regression determine that mobility is affected by the ability to dispose of the current home. More specifically, facing negative equity situation or low equity has a negative impact on accepting relocation. Further, affordability, housing prices and unemployment and availability of funds also play a significant role in the ability to relocate and accept a transfer.

The remainder of this paper proceeds as follows: the Migration and Job Mobility section discusses the concept of migration and job mobility while the Corporate Relocation section focuses on one specific type of mobility - corporate transfers- and provides an overview of corporate relocation and more specifically describes the homesale process as

4 part of the relocation. The Literature Review section presents previous studies and outlines the theoretical grounds of our model and hypotheses. The Model and Hypotheses section presents in detail the various models and outlines our main hypotheses. The section

Empirical Models and Variables presents the factors affecting the decision to relocate and our selection of variables and the design of the models. The sampling process is presented in the Data & Sampling Process section while the Empirical Results section presents and discusses our empirical findings. The Conclusion section concludes our study.

CHAPTER II

MIGRATION AND MOBILITY

Migration as a result of job mobility has always been amongst the top ten areas of interest for many researchers. The concept of mobility as a way to capture opportunities has played a central part in forming the national identity of the United States and the

American Dream (Moscarini and Thompson, (2007)). Most importantly, mobility is a way to reallocate resources as a result of natural business and economic progress and fluctuations. One of the main sources of data related to mobility comes from the Current

Population Survey (CPS). The survey is designed to provide information on geographical mobility. It is an annual survey of about 50,000 households conducted by the Census

Bureau for the Bureau of Labor Statistics. The study provides information regarding migration on both national and regional level from 1947 to present. The question regarding reasons to move has been added to the survey in 1998. The data is collected as part of the

Annual Social and Economic Supplement (ASEC) and includes data on the annual rate of moving, and the characteristics of movers as Ill as by type of move. In 2005-2006, 31% of the job related moves are homeowner moves2 while in 2007-2008, the percentage drops to 25% and the most recent 2012-2013 survey shows only 23% of the job related moves

2 Homeowner moves related to moves where the person owns a home in the departure location

6 are homeowner moves. In regards to gender and marital status, the survey provides further information related to mobility. In the 2005-2006 survey, 55% of the job related moves indicate Male as sex; the percentage holds in the 2007-2008 survey while in the most recent

2012-2013 survey the percentage drops to 51% showing that mobility has increased within the female population of migrants. In regards to marital status, in the previous two surveys,

39% of the job related migrations are attributed to migrants that indicated married status while in the most recent survey 36% vs 33% of job related moves indicate single status.3 It can be concluded that homeowner status, marital status and gender show interesting dynamics changes after the financial crisis.

The studies on migration follow two main avenues- studies focusing on aggregate mobility data and studies looking into the individuals' determinants of geographical mobility. Ann P. Bartel studies the link between migration and job mobility in different stages in the life cycles using National Longitudinal Survey (NLS) of Mature men and NLS of Young men. She examines the effect of job mobility on migration and more specifically the effects of wages, family status, education and length of residence in current location on the decision to migrate. Bartel (1979) finds statistically significant impact of job mobility on decision to migrate while length of residence has a negative effect on mobility. One of the possible explanations is that the employee has built enough capital stock in the current location that prevents them from moving. In the context of this study, the research validates the assumption that home ownership and housing market do have an effect on decision to migrate and switch jobs either via transfer or job separation. The next section describes in

3 The data is extracted from the CPS survey table data- table 31 from 2005-2006 survey and table 23 for 2007-2008 and 2012-2013 surveys http://www.census.gov/hhes/migration/data/cps. html

7 detail the corporate transfer relocation process and more specifically the process of selling the home within the relocation move.

CHAPTER III

CORPORATE RELOCATION OVERVIEW

Talent management is a big part of corporations' Human Resource Departments' responsibilities. Employees are one of the biggest assets of an organization and as such require proper human resource planning and development. The new global business environment requires companies to maintain a mobile workforce. As a result, corporations engage in a formal management and development of corporate relocation programs within the realm of their Human resource departments to facilitate the talent deployment and management.

3.1 Corporate Relocation Process

Employee relocation is driven by many internal and external processes and it is quite a complex undertaking. It starts within the actual corporation but often the delivery of the relocation services is outsourced to relocation management companies (RMCs) who administer the relocation program and benefits on behalf of the employer and assist the relocating employee and their families with all aspects of the move. Relocating an employee can be done not only within the country, i.e. domestic move, but also internationally. The Employee Relocation Council (ERC) survey shows that in 2013 97% of the 84 companies that participated in the survey outsource their homesale program to a

9 third party relocation management company (RMC) and over 61% outsource the management of the full relocation program.

The relocation process starts with identifying a need for certain talent and skills and determining if local talent pool can satisfy those needs. If it is determined that an existing employee or a new hire from different geographical location will be a better fit to fill in those local talent needs, then business managers will work with their HR teams or Global

Mobility teams to facilitate the relocation process. A job offer is presented to the identified existing employee or new hire and on average, they have two weeks to consider the transfer offer. Most employers expect the relocating employee to report to the new job on average

33 days after accepting the offer. 4 As soon as the offer letter is executed, the mobility team will initiate the moves with a third party RMC to start facilitating the process. If the programs are managed in house then the internal team will guide the employee through the process. This study will focus on the process managed by a third party relocation company since this is the prevalent method of managing the relocation process.

Generally, the corporation will have formal policies outlining the benefits for which the relocating employee is eligible. Most companies will use multiple or tiered polices using job or salary level as criteria to provide different benefit packages. The recent 2013

ERC survey shows that 84 % of companies provide tiered policies and most of them have more than three tiers. A typical domestic move relocation package will include benefits to address the move of household goods, finding a new home to purchase or rent, temporary living accommodations in the new location and a formal home marketing program to assist in selling the residence in the old location. The recent ERC Relocation Assistance

4 Source for the statistics is 2013 Worldwide ERC U.S. Transfer Volume & Cost Survey

10 executive digest of U.S. domestic transferee employees’ programs shows that over 75 % of members who offer homesale benefits also offer formal home marketing assistance programs to help with the disposition of the home and another 11 percent offer such benefits to some employees.

In addition, to address the real estate crisis, many companies have re-evaluated their policies of assisting employees with the losses incurred when selling their homes through an increased loss-on-sale reimbursement or incentives such as home sale bonus to find a buyer during the self-marketing period (2013 Worldwide ERC survey).

3.2 Relocation HomeSale Process

In 2011, the annual spent in the U.S. on corporate relocation by Worldwide ERC member corporations was $9.3 billion and the annual number of U.S. domestic transfers was around 184,433 relocations. Relocating an employee is quite costly especially if the employee is a home owner. On average the cost of a U.S. domestic transfer of a current employee who owns a home was $91,528 compared to the cost of $24,714 for a current employee who rents. The major difference between the two cost numbers is the cost of selling the existing residence. The average homesale assistance cost in 2012 is around

$45,000, a 12.5% increase from previous survey in 2012. This is a significant investment and cost for the employer. As a result, there is a well-established process for selling a home as part of the relocation. 5 The average purchase price of the homes going through the homesale program is $340,592, while the median price is $303,000.

5 The source of the information is 2013 Worldwide ERC U.S. Transfer Volume & Cost Survey

3.2.1 Overview of the Relocation HomeSale Process

Companies can offer different programs and tools to assist employees with the disposition of their home in the old location. The recent survey conducted by ERC shows that on average employers expect the relocating employee to start in the new location within four weeks after accepting the offer. This puts significant pressure on the employee to sell their home before moving and with the turmoil in the housing market, it is quite obvious that employee's perception of their ability to dispose of their house has a significant impact on their mobility. In addition, selling a home has significant tax implications. The homesale process tax treatment is governed by IRS Rulings 72-339 and 2005-74 and warrants a discussion in a separate section but the general rule is that the sale of a residence through two separate transactions generally results in more favorable tax treatment than disposal through one sale. As such either the employer or the RMC serve as buyer in the first transaction where a bona fide offer is presented to the employee. Subsequently, the employer or the RMC sell the property to a third party buyer, i.e. outside buyer. It is very important that the homesale program is structured properly as two separate sales. Appendix

A discusses the 11 key elements important to create a complaint program.

Some of the most common programs offered as part of a relocation program are reviewed below. A well- structured homesale program not only benefits the relocating employee but could also provide cost benefits to the employer.

Appraised Value Sale

This homesale program requires an appraisal of the employee's residence by two or more independent appraisers. In general two appraisals are ordered unless there is a divergence of 5-10 percent (this is dependent on the employer's policy), in which case a

12 third one will be ordered. Subsequently, the employer or the RMC will extend a guaranteed offer to purchase the home either by the employer or the RMC acting on its behalf; the amount of the guaranteed offer is generally the average of the two appraised values.

Generally, there would be a requirement for marketing a home prior to an offer being extended. As part of the program, the transferees (relocating employees) will be required to market their home for a specified time as indicated by their employers’ homesale program. If accepted by the employee, the employer or RMC purchases the home and starts the process of finding a bona fide third party buyer called an outside buyer. Once the employee accepts the offer, they are not affected by the ability of their employer or the

RMC to find a buyer or the sale price of the second sale. In essence, the RMC serves as an intermediary guiding the relocation process and facilitating the two separate homesale processes.

Amended Value Sale (AVS) or Amended Value Option (AVO)

This type of homesale program requires the appraisal of the employee's residence by two or more independent appraisers and an offer to purchase the home is made by either the employer or RMC. The offer amount is generally an average of two appraisals values unless a difference such as 10 percent between the two exists, then a third appraisal will be ordered.

Before accepting the offer, the employee markets the home to determine whether a higher price can be obtained. If a third party buyer, i.e. outside buyer makes a bona fide purchase offer, the employer or RMC will raise its offer to equal the outside buyer's, hence the term ``amended value''.

To satisfy the two independent sale requirement of the IRS, the employee contracts to sell to employer/RMC, who then enters into its own listing agreement with a real estate broker, attempting to contract with and sell the home to the same third party buyer identified by the employee. Thus, the relocating employee is unaffected by the outcome of the second transaction, as in the previously discussed appraised value sale program. In general, the employer will require a certain period of marketing time before appraisals are performed and an offer is extended if an outside buyer is not found.

Buyer Value Option (BVO)

This type of homesale program differs from the two above because no appraisals are performed, and no guaranteed offer is made to the employee. On the contrary, the employee markets the house and seeks a bona fide buyer’s offer in the market place. It is important to note that employers offer formal home marketing program so in most cases the RMC will assist the employee with marketing the home and help with selecting a proper agent. Once an outside buyer offer is received, the employer or RMC makes an offer to purchase the home at “buyer value,” as established by the outside offer. Then similarly to the two programs above, the employee contracts to sell to the employer/RMC, who then enters into their own listing agreement with a real estate broker to contract with and sell the home to the same outside buyer identified by employee.

Direct reimbursement

Assisted sale or direct reimbursement sale requires the relocating employees to sell their residence directly to a third-party buyer. Subsequently, the employer reimburses them for some or all of the associated costs of sale. Additional taxation may apply in this type of sale since this type of program does not show two separate transactions.

Direct offer

In this type of homesale, the employer or relocation company buys the employee's residence at a price in excess of fair market value.

Figure 2 shows the percentage of types of homesale transactions reported by the 86 companies participating in the 2013 ERC survey. Amended value is the more prevalent with Appraised and Buyer Value options a close second and third, respectively. The type of homesale program has a direct impact on the process of selling the transferee’s house in the old location thus it is an important factor in the decision to relocate. The next section will focus on the home marketing process.

Figure 2. Types of HomeSale Transactions

3.2.2. Home Marketing Process

To complete the discussion of the homesale process it is very important to discuss the home marketing process, which includes determining listing price, selecting a proper broker and agent and discussing the determination of property value through the appraisal process.

Since this study is utilizing private RMC homesale transactional data, I will focus on presenting the home marketing process as part of the services offered by the RMC.

Well-established marketing assistance process provides assistance and advice to the relocating employee and helps employers and RMCs with the reduction of properties not being able to be sold and going into inventory6. The process starts with the proper selection of two experienced brokers through the preferred network of the RMC. The brokers will assign an agent based on the information received from the RMC. Usually, the relocating employee will meet with two agents before selecting the listing agent. The agent’s responsibility is to gather information necessary to complete an ERC Broker’s Market

Analysis and Strategy Report (BMA)7 . Producing a BMA will enable the agent to compile a comprehensive analysis to arrive at a projected sale price (most probable sale price) and recommend a listing price. The results of the BMA are shared with the RMC and reviewed with the relocating employee. The RMC will also make a recommendation and subsequently the employee will select their listing agent. The selected agent is notified and the listing is taken, incorporating an ``exclusion clause'' which will allow the employee to sell the property to the RMC as an agent of the employer. It is important to note, that many employers will have guidelines around determining the listing price based on the BMA values to encourage proper pricing at time of listing which increases probability of selling

6 The term “inventory” means that a bona fide third party buyer, i.e. outside buyer, has not been found prior to presenting the transferee with an offer so the employer will have to purchase the home from the employee and service and market the home until such third party buyer is found. The sale is structured is such as a way to show in good faith the two sale transactions as governed by the 11 elements of a complaint homesale program. 7 In 1989, the BMA form was introduced to be used in conjunction with the appraisal process during a traditional corporate homesale program. The primary reasons of the BMA process is to provide valuable information as part of the overall homesale assistance program and assist in devising home-marketing strategy. The BMA provides the most likely sales price ``as is'' and ``with repairs and improvements'' as well as the most likely net price ``as is'' and ``with repairs and improvements'' assuming a reasonable marketing time which is typically no more than 120 days. It is a three-page form supplying such information as likely financing, property details and local market recent sales. It enables the real estate broker to analyze and report on the subject property’s condition, other listed properties with which the subject must compete, any recently sold properties and most importantly the marketability of the subject property.

16 the property during the employee marketing time. The above described pre-marketing process is designed to help achieve open market sales and the realization of maximum market value. The next section focuses on the appraisal process. It is important to present the framework of obtaining appraised value since this variable is part of determining the equity that a transferee holds.

3.2.3 Relocation Appraisal

The timing of conducting the appraisal of the property is driven by the homesale program benefits provided by the employers- appraisal can be performed 30, 60 or 90 days after listing or immediately prior to listing a property. In the case of RMC managing the process, the appraisals will be ordered by the RMC and reviewed by the RMC with the relocating employee. It is important to note that appraisals for relocation and refinancing are different in their intended use. Each appraisal is based on a different definition of value, with its own specific guidelines that affect the development of the valuation.

The relocation appraisal is based on an anticipated sale of the property during a defined, reasonable marketing period and the analysis considers what market trends and conditions may impact the property while it is marketed. The mortgage appraisal is based only on a retrospective analysis of past sales transactions and reflects normal market conditions. While the procedures followed in each type of appraisal are similar, an appraisal to facilitate corporate relocation must reflect ``Anticipated Sales Price,” not

“Market Value.'' Relocation appraisers use forecasting to determine the ``Anticipated Sales

Price ''. The process of forecasting involves analyzing historical trends and current factors as a basis for anticipating market trends. The trends into consideration include such factors as supply and demand characteristics, days on the market, interest rates, seasonal market

17 fluctuation, new construction, pending sales, absorption rates, and the overall mood and health of the market. The forecasting analysis considers the impact these trends will have on the property during its market exposure period. A forecasting adjustment is then applied to reflect the result of this analysis on the property’s marketing time and sales price. The adjustment could be positive indicating an increasing market, negative indicating a declining market, or a zero adjustment indicating a relatively stable market.

Relocation appraisal is different from the Broker's Market Analysis because the intended use of a BMA is to develop the ``Most Likely Sales Price'' and the ``Most Likely

Net Sales Price'' of the subject property in `às is'' condition and with ``repairs and improvements'' while the `Ànticipated Sales Price'' in the appraisal report presents an opinion based on `às is'' condition and does not consider the impact of any near term improvements/modifications to the property.

3.2.4 Tax Implication of HomeSale Process

Selling a home as part of a qualified relocation has tax implication and the type of the homesale program plays a significant role in determining tax liability. As previously discussed, if the program clearly shows two separate sales, then the proceeds and costs of said program will generally result in more favorable tax treatment than if the home is sold through direct reimbursement or direct offer. For example, if the transferee enters into an

"assisted'' sale which means that only one sale actually exists between the employee and a buyer then by design the closing costs and a broker commission will be incurred by the assignee and reimbursed by the employer resulting in a taxable liability subject to taxes and withholding. In a different scenario, when the home is appraised by two independent appraisers and the employer offers an acquisition price to the employee at the average of

18 the appraised values as part of an appraised value homesale program, all the costs of sale imposed on the seller under local law and custom such as title cost, transfer/exercise tax, broker commission etc. are directly covered by the employer/RMC. As part of the two sales process, the employee sells to his or her employer and no longer has any control or have possession of the house. Subsequently, the employer lists the residence with a real estate broker. When the property is sold to an outside buyer, the employer pays a sales commission and other closing costs. None of those costs are considered taxable income to the employee (IRS agreement with two sale characterization: Rev. Ruls. 72-339 and 2005-

74). That is why many employers outsource the management of the process to a third party

Relocation Management Company to ensure compliant process and two-sale transaction.

This study focuses on two-sale transactions. The transactional homesale data gathered as part of the process allows us to determine the equity the transferee holds at the time of the sale.

CHAPTER IV

LITERATURE REVIEW

The main goal of this study is to examine the relationship between the decision to relocate and the health of the housing market. I posit that the health of the housing market has a significant effect on the ability of employees to relocate. A well-functioning, healthy housing market not only aids the participants to accumulate wealth but also helps the job market run more efficiently by widening the pool of participants for a better job matching process. Research on residential mobility examines the probability of a move being conditional on various economic factors affecting housing demand such as income, and price-volume correlations while accounting for certain demographic characteristics.

Ferreira, Gyuorko and Tracy (2010) revisit the literature on lock-in effects and provide new evidence around the impact of negative equity and increasing interest rates on mobility using data from the American Housing Survey from 1985-2007. They determine that mobility can fall during the periods of housing busts because of financial constraints and loss aversion mechanisms. The results show that negative equity can reduce the 2 year mobility rates by 4 percent and the estimates show that overall household mobility can be reduced by 35 percent if households face negative equity situations. Similarly, they determine that a $ 1000 increase in annual real interest rate costs decreases mobility by 12

20 percent. Chan (2001) confirms the notion that equity constraints can lead to reduction in mobility from one-quarter to one-third lower for the New York City metropolitan area.

Henley (1998) explores the relationship between residential mobility and low or negative housing entity. He also investigates if the labor market flexibility is affected by the stagnant housing market by examining if households are prevented from taking on an opportunity in the labor market (Henley, p. 441). His main focus is the mobility in the

United Kingdom but he draws on previous research done for the U.S. The results of the study show that housing wealth is an important factor in explaining mobility and that negative equity has a significant adverse effect on residential mobility and on the housing market health. Henley further concludes that house owners face a double hurdle and will forgo moving to more stable areas of employment even in situations where they face high unemployment rates.

Weaton (1990) shows that buyer-seller matching efficiency is linked to the volume of affordable houses for sale since sellers facing a negative equity situation and possible loss fail to list the homes at a price that matches the buyers' reservation levels. Case and

Schiller (1989), on the other hand, show that the rational response to a downward housing market is to hold on to the existing properties in anticipation of future positive returns.

This study draws on the previous research by further exploring the relationship between job mobility and the housing market while examining the decision of employees to accept an actual offer from their employer to move. I posit that employees will not engage in relocation activity if they are faced with a depressed housing market and negative equity or loss on sale situation. They may have to forgo an advantageous job opportunity

21 due to their inability or perceived inability to sell their current residence. In the next section,

I define the model and formally state the hypotheses.

CHAPTER V

MODEL AND HYPOTHESES

5.1 Theoretical Model

I proceed by establishing the theoretical model. I posit that an employee will accept a corporate transfer offer after examining various economic and personal factors. A main factor in this decision will be the employee’s ability to sell his or her current residence in order to move into the new location. As a result, variables like negative equity and equity as percentage of home value will play a significant role in the decision-making. In addition, housing market variables such as price, volume and affordability as well as various economic factors will also affect the probability of an employee relocating. Finally, to address the regional variation of housing markets factors and specific location factors such as community, safety etc., the model will control for departure state and year of the transaction. Previous studies show the importance of demographics in such models, so I will incorporate marital status and gender into the models to examine their impact on mobility.

I summarize the factors in the model below

Pr (Corporate Transfer) = f (Equity, HomeSale Program Design, Housing Market Health, Demographics, Destination Factors, Control Variables) Where

 Equity is defined as the actual or estimated equity at time of sale to the

employer/RMC. This study will further examine the direct impact of negative

equity as well as low equity defined as percentage of current home value

determined by a sales price or average appraised value

 HomeSale Program Design- the type of homesale program as well as additional

homesale benefits such as covering loss on sale or providing a bonus for selling the

home within a predetermined period have a direct impact on the decision to move.

If the transferees know that their employer will purchase the house or cover their

loss on sale, the financial constraints have been eliminated and their willingness to

relocate increases

 Housing Market Health – various factors such as HomeSale prices, affordability,

as well as availability of funds and unemployment rates gave direct economic

impact on mobility and the ability to relocate

 Control variables such as departure state and year address the regional variability

as well as the trending as part of modelling

 Demographics such as gender and family status also have an effect on the decision

to move

 Destination factors such as education, crime rates and local housing prices and

unemployment have effects on the willingness to relocate

5.2 Hypotheses

To examine the effect of the housing market on the probability of employees to accept a corporate transfer, I establish two main hypotheses. The first hypothesis examines

24 the financial constraints of having low equity or negative equity on the ability to relocate while the second hypothesis examines the housing market dynamics on mobility.

H1: The probability of accepting a corporate transfer is negatively affected by the negative equity of the homeowner

H2: The efficiency and affordability of the housing market affects the probability of relocating.

5.3 Methodology

Literature shows two main ways to determine probability of relocating- probit model or logistic model and proportional hazard model. Ferreira et al. (2010) similarly to

Han and Hausman (1990) employ the probit regression over the proportional hazard framework. They determine that the Probit framework is better in handling flexibility while providing the same controls. They determine that there is no specific reason why one is superior to the other. This study will use the stepwise logistic regression within the probit modelling in SAS. Probit regression, also called a probit model, is used to model dichotomous or binary outcome variables. In the probit model, the inverse standard normal distribution of the probability is modelled as a linear combination of the predictors. I will use the logistic regression within the probit model for ease of interpretation. I will also include various lags for our continuous market indicators. This poses some challenges with multicollinearity. I will examine the Vector inflation factors (VIF) as well as the conditions index to detect multicollinearity. The next section discusses in further detail the background behind the factors included in the model.

CHAPTER VI

EMPIRICAL MODELS AND VARIABLES

6.1 Factors Affecting the Decision to Relocate

Factors affecting the decision to accept a corporate relocation can be broken into several main categories. Each of the categories is discussed in a separate sub-section and presents the motivation to include variables that proxy those factors in the study.

6.1.1 Personal and Family Factors

This category houses bio-demographic factors such as age, work tenure, family status, education, number of children etc. Brett and Reilly (1988) identify seven demographic characteristic that affect the job transfer decision-- age, marital status, working spouse, family stage, number of children at home, income and education. The results of the principal component analysis show that decision to relocate and accept a job transfer is associated with some key demographics, career attributes and the employee and spouse's attitude towards relocating. Family status and number of children have negative effect on relocation, the willingness to move and high job involvement increase the probability of accepting a transfer. Age, working spouse and education did not have a significant impact on the decision making process.

Eby and Russell (2000) also examine the willingness of employees to relocate within a heuristics framework. They examine background factors such as marital status,

26 age and children as well as employees' perceptions and attitudes toward their job, career and moving. They also examine the spouse's attitude towards relocating. Contrary to Brett and Reilly (1988), Eby et al. determine that spousal attitude is the single most important predictor of employee willingness to move. They also determine that younger, single- income earners, with no children are more willing to move for a job.

6.1.2 Economic Factors

Economic factors such as interest rates, regional economic indicators, mortgage rates, unemployment rate etc. also play a role in the decision to relocate. There are many studies exploring the effects of national and regional economic factors on housing prices.

Reichert (1990) studies the reaction of housing prices to local and national economic factors such as population, employment rate, mortgage rate, income, and leverage proxied by loan to value ratio for each region. The largest impact variables are population, employment and permanent income. The housing prices show different sensitivity based on region. For example, employment rate changes have more impact in the Middle Atlantic region while mortgage rate changes affect the New England's housing market the most and permanent income has the most impact on the West. The overall conclusion is that a combination of both local and national factors work together simultaneously to influence housing prices. The local factors use their unique patterns upon broad price movements generated by changes in interest rates at the national level. That is why the model in this study controls by departure state.

6.1.3 Compensation and Career Opportunities

The probability to relocate is also influenced by situational variables such as salary, degree of job involvement, visibility to organizational decision makers, compensation etc.

(Gould and Penley (1985), Landau and Hammer (1986), Noe, Steffy and Barber (1988)).

Gould and Penley (1985) determine that higher than average income is positively related to the willingness to relocate due to the opportunities available to higher salary individuals.

6.1.4. Destination Location Factors

New community characteristics are another set of factors that affect the decision to relocate. Noe and Barber (1993) posit that employee’s adjustment to the new location following a transfer is critical to the employee satisfaction and retention. That is why the authors examine relocations to similar and dissimilar communities and determine that employees are reluctant to accept moves between dissimilar communities. They also confirm that factors such as personal and family characteristics as well all as community attachment work history and career factors have an impact on the willingness to relocate.

6.1.5 Housing Market Factors

The last category of determinants is central to this study. I posit that housing market factors such as negative equity, housing market volume, pricing and affordability play a central role in the willingness of employees to relocate and thus significantly affect the acceptance of corporate transfers.

Feldman and Bolino (1998) discuss the role of real estate purchases and sales on individual and corporate transferees. They posit that real estate problems may be

28 exacerbated for corporate transferees because the employees may be asked to move to a less expensive community facing a deflated housing market or to a more expensive community with inflationary home prices. They suggest that future research on corporate relocation has to take into consideration the liquidity factors, corporate real estate homesale programs and the differences in price structure between the new and current locations of the employee.

6.2 Empirical Models and Variable Discussion

6.2.1 Empirical Models

To be able to separate the impact of housing market conditions from the individual household financial constraints, I will create two sets of models.

In Model 1 related to Hypothesis 1, I will test the corporate transfer acceptance by performing logit regression on LOWEQTY and NEGEQTY to isolate the effect of these independent variables.

In Model 2, I will introduce the Housing market variables to determine their incremental effect on the acceptance of corporate transfer. The expectation is to confirm the ERC survey results where employees decline to accept the offer to relocate because of market conditions in both departure and destination location. If the employees believe that they cannot sell their house due to bad housing market conditions such as declining seller prices, increased days on market exacerbated by the possibility of taking a loss which may result in a negative situation, they will not accept the relocation resulting in a cancelled move. In addition, if they are moving to a location with higher housing prices, affordability and liquidity start to play a more prominent role. Inability to secure a loan or find suitable

29 housing arrangements in the new location can weigh in heavily and cause the employee not to accept the corporate transfer.

I now formalize the four models below. Each model has two sub-models related to the two categorical variables NEGETY and LOWEQTY. Since they have very high correlation, including them in the model simultaneously will not produce reliable estimates.

Therefore, I test each of the models separately.

Model 1a and Model 1b test the first hypothesis related to transferees facing negative situation. Model 3a and Model 3b are the expanded version of Model 1a and

Model 1b respectively where I include additional demographic variables to determine how individual demographic factors have an impact on the decision to transfer. Similarly,

Model 4a and Model 4b are expanded versions of Model 2a and Model 2b where I introduce the same demographic factors in testing Hypothesis 2. Table 1 provides an explanation of each variable and its type. In addition, the table also shows the expected sign of the impact on the willingness to accept a transfer. I also introduce models 5a and 5b which incorporate certain destination factors in testing Hypothesis 2.

Model 1a

TFR= α +β1 NEGETY + β2 HSTYPE +ε

Model 1b

TFR= α +β1 LOWEQTY + β2 HSTYPE +ε Model 2a

TFR= α +β1 NEGETY + β2 HSTYPE + β3AFFORDt + β4 FLOWt + β5 UNEMPLOYt + β6 CHSIt + β7 LENDSTDt + β8 AFFORDt-1 + β9 AFFORDt-2 + β10 FLOWt-1 + β11 FLOWt-2 + β12 UNEMPLOYt-1 + β13 UNEMPLOYt-2 + β14 CHSIt-1 + β15 CHSIt-1 + β16LENDSTDt-1 + β17 LENDSTDt-2 + Year+ Departure State+ ε Model 2b TFR= α +β1 LOWEQTY + β2 HSTYPE + β3AFFORDt + β4 FLOWt + β5 UNEMPLOYt + β6 CHSIt + β7 LENDSTDt + β8 AFFORDt-1 + β9 AFFORDt-2 + β10 FLOWt-1 + β11 FLOWt-2 + β12 UNEMPLOYt-1 + β13 UNEMPLOYt-2 + β14 CHSIt-1 + β15 CHSIt-1 + β16LENDSTDt-1 + β17 LENDSTDt-2 + Year+ Departure State + ε

Model 3a TFR= α +β1NEGETY + β2HSTYPE + β3 BENFT + β4FMLY + β5GENDER + β6MALE*FMLY + β7FML*FMLY + Year+ Departure State + ε

Model 3b TFR= α +β1LOWEQTY + β2HSTYPE + β3 BNFT + β4FMLY + β5GENDER + β6MALE*FMLY + β7FML*FMLY + Year+ Departure State + ε

Model 4b TFR= α +β1 NEGETY + β2 HSTYPE + β3AFFORDt + β4 FLOWt + β5 UNEMPLOYt + β6 CHSIt + β7 LENDSTDt + β8 AFFORDt-1 + β9 AFFORDt-2 + β10 FLOWt-1 + β11 FLOWt-2 + β12 UNEMPLOYt-1 + β13 UNEMPLOYt-2 + β14 CHSIt-1 + β15 CHSIt-1 + β16LENDSTDt-1 + β17 LENDSTDt-2 + β18 BENFT + β19FMLY + β20GENDER + β21MALE*FMLY + β22FML*FMLY + Year+ Departure State + ε

Model 4b TFR= α +β1 LOWEQTY + β2 HSTYPE + β3AFFORDt + β4 FLOWt + β5 UNEMPLOYt + β6 CHSIt + β7 LENDSTDt + β8 AFFORDt-1 + β9 AFFORDt-2 + β10 FLOWt-1 + β11 FLOWt-2 + β12 UNEMPLOYt-1 + β13 UNEMPLOYt-2 + β14 CHSIt-1 + β15 CHSIt-1 + β16LENDSTDt-1 + β17 LENDSTDt-2 + β18 BENFT + β19FMLY + β20GENDER + β21MALE*FMLY + β22FML*FMLY + Year+ Departure State + ε

Model 5a

TFR= α +β1 NEGETY + β2 HSTYPE + β3AFFORDt + β4 FLOWt + β5 UNEMPLOYt + β6 CHSIt + β7 LENDSTDt + β8 AFFORDt-1 + β9 AFFORDt-2 + β10 FLOWt-1 + β11 FLOWt-2 + β12 UNEMPLOYt-1 + β13 UNEMPLOYt-2 + β14 CHSIt-1 + β15 CHSIt-1 + β16LENDSTDt-1 + β17 LENDSTDt-2 + β18VIOL_DIFF + β19ROB_DIFF + β20BULG_DIFF + β21EDU_DIFF + β22HPI_DIFF + β23UNEMPLOY_DIFF+ Year+ Departure State + ε

Model 5b TFR= α +β1 LOWEQTY + β2 HSTYPE + β3AFFORDt + β4 FLOWt + β5 UNEMPLOYt + β6 CHSIt + β7 LENDSTDt + β8 AFFORDt-1 + β9 AFFORDt-2 + β10 FLOWt-1 + β11 FLOWt-2 + β12 UNEMPLOYt-1 + β13 UNEMPLOYt-2 + β14 CHSIt-1 + β15 CHSIt-1 + β16LENDSTDt-1 + β17 LENDSTDt-2 + β18VIOL_DIFF + β19ROB_DIFF + β20BULG_DIFF + β21EDU_DIFF + β22HPI_DIFF + β23UNEMPLOY_DIFF+ Year+ Departure State + ε

The next sections lay the literature, search support, and provide reasoning of including certain factors in the model. Table I provides an explanation for each of the variables of the model, their expected sign and type.

Table I Variable Legend

Variables Type Expected Explanation Sign

TFR Categorical TFR=1 when employee has accepted transfer, 0 otherwise

NEGETY Categorical - NEGETY=1 if transferee faces negative equity, otherwise 0

LOWEQTY Categorical - If equity is less than 5% of the value of the house, the value assigned is 1 indicating the transferee is “under water”, otherwise 0

HSTYPE Categorical + HSTYPE= 1 if employer offers guaranteed Homesale benefits, otherwise 0

AFFORD Continuous + The 3-month moving average of the Affordability Housing Index published by NRA

FLOW Continuous + The monthly interpolated value of the Federal Reserve quarterly Households and nonprofit organizations; home mortgages; liability flow of funds

UNEMPLOY Continuous - Monthly value of the BLS unemployment rate

LENDSTND Continuous - Net percentage of domestic banks tightening standards for C&I loans to small firms

CHSI Continuous ? S&P500/Case& Shiller 20 City Monthly Index- 3- month moving average

BENFT Categorical + BNFT=1 if the employer offers a HomeSale Bonus or Loss on Sale coverage as part of the homesale benefits, otherwise 0

FMLY Categorical - FMLY= 1 if the transferee has indicated a spouse/partner, otherwise 0 + GEN=1 if the transferee has indicated gender to be male and 0 for GENDER Categorical female

MALE*FMLY Interaction ? 1 if the transferee is a male and has indicated spouse/partner and 0 otherwise

FML*FMLY Interaction ? 1 if the transferee is female and has indicated spouse/partner and 0 otherwise

HPI_DIFF Continuous ? The difference between the monthly value of the Home Purchase Index published by the Federal Housing Finance Agency of the Destination State and Departure State

EDU_DIFF Continuous + The difference between the Per Pupil Expenditure Amounts of the Destination and Departure State. (Source: Annual Survey of Local Government Finances - School Systems. by the U.S. Census Bureau) Note: Data was converted to monthly frequency by interpolating using cubic spline function

UNEMPLOY_DIFF Continuous - The difference between the Monthly Unemployment rate of the Destination and Departure State

VIOL_DIFF Continuous - The difference between the reported annual Violent Crime Rate of the Destination and Departure state (Source: Uniform Crime Reporting Statistics - UCR Data Online ( FBI)) Note: Data was converted to monthly frequency by interpolating using cubic spline function

ROB_DIFF Continuous - The difference between the reported Robbery Crime Rate of the Destination and Departure state (Source: Uniform Crime Reporting Statistics - UCR Data Online ( FBI)) Note: Data was converted to monthly frequency by interpolating using cubic spline function

BULG_DIFF Continuous - The difference between the reported Burglary Crime Rate of the Destination and Departure state (Source: Uniform Crime Reporting Statistics - UCR Data Online ( FBI)) Note: Data was converted to monthly frequency by interpolating using cubic spline function

Year Control To address trending, year is included in the logit model

Departure State Control To address departure state variability and regional factors, departure state is included in the logit model as control variable

6.2.2 Dependent Variable

The dependent variable -Corporate Transfer is a categorical variable. If the employee accepts the offer to relocate, the dependent variable is assigned a value of 1, otherwise the value is 0.

6.2.3 Independent Variables

NEGEQTY is a categorical variable that takes the value of 1 if the employee has negative equity and 0 otherwise. It is determined by calculating the equity that a transferee holds in the house. For relocations with completed homesale, we use the actual equity paid to the employee as part of the two-sale process. For the rejected relocations, the equity is calculated as the difference between the original purchase price and the appraised value or acquisition price offered by the relocation provider or the employer. The prospect theory presents another factor that affects mobility and the decision to relocate and that is the concept of loss aversion. Even if the household is not financially constrained, the employee may be less mobile if the nominal loss aversion causes the household not to sell their home after price has fallen (Genesove and Mayer 1997, 2001) and Engelhardt (2003). Ferreira,

Gyourko and Tracy (2010) state that homeowners who have negative equity are one third less likely to move. However, the theoretical predictions of the impact of negative equity are not conclusive. On one hand, homeowners will be constrained because of lack of liquidity so they will not be able to relocate without defaulting on the loan or taking a loss.

As a result, their ability to meet the lending requirements for down payment will be

33 significantly decreased making it difficult to find a suitable housing position in the new location.

On the other hand, Shulhofer-Wolf (2012) directly criticizes the previous study and attacks the use of the data and the process of sample creation because the authors systematically drop some negative-equity homeowners’ moves from the data. Once those observations are included, the new study shows the opposite effect of negative equity.

Using the new data sample, the author determines that homeowners with extremely negative equity are more mobile than those with slightly negative equity; this is in contrast to the Ferreira et al. findings. Furthermore, Shulhofer-Wolf (2012) warns that due to recent changes in economy as well as the fact that negative equity was quite unusual in the period studies by Ferreira et al. (2010), the results may be different if the study were to use more recent data.

I posit that negative equity acts as a financial constraint further limiting the ability of relocating employees to sell their homes especially if their employer’s homesale assistance process does not provide any reimbursement of loss on sale or reimbursement of negative equity. In those cases, the relocating employee as a seller has to come up with additional funds to the closing table and absorb the loss. Secondly, negative equity can pose as an additional threat of now meeting the down payment requirements for securing a residence in the new location and thus making it difficult to purchase a new residence.

I further want to explore if low equity also has a determinant factor in the mobility decision. I present a threshold of 5 percent to determine if equity as percentage of house value less than 5 percent still represents a financial constraint. I chose 5 percent since buying a new home in the new location might require a deposit. I identify this variable as

LOWEQTY. Additionally, I perform robustness check analysis by setting up a threshold of 7.5 percent (long-term average cost of home sale costs of the data set) and 10 percent threshold to examine if the model is sensitive to the threshold value.

Housing Market Variables

Housing market variables, more specifically conditions in local and national markets, have a significant impact on mobility in general. Differences in volume, turnover rates and house prices have a significant impact on micro level mobility and play an important role in the decision making process of the employee (Moore and Clark, 1990).

Differences on a local level in certain economic factors also affect the ability to move due to different price dynamics (Strassman, (2000), Dielman (2000), Reichert (1990)). Factors such as new construction levels and population mix lead to differences in economic and demographic growth between cities, which results in different turnover rates (Strassman,

2000). In addition, affordability and price levels in different cities also lead to changes in turnover, volume and price resulting in different housing market dynamics (Strassman,

(2000), DiPasquale and Wheaton (1996), Abraham and Hendersholt, (1996)). In the study models, I include S&P500/Case Shiller 20-city Monthly Index to proxy housing market pricing. The National Realtors Association Monthly Affordability index is used as a proxy for affordability in the housing market. To minimize the noise in the S&P500/Case Shiller

20-city Monthly Index and the Affordability Index, the 3-month moving average is used for each monthly observation.

I also employ the Federal Reserve Flow of Funds, which serves as a proxy for liquidity and is measured on a quarterly frequency, so to be able to merge with the monthly data, I interpolate into a lower monthly frequency.

The Quarterly Senior Loan Officers Opinion Survey provides valuable insight on how lending officers react to various factors such as monetary policy, stability of the markets etc. by adjusting their lending practices (LENDSTND). The data related to the

“Net percentage of domestic banks tightening standards for prime mortgage loans” is not available until 2007Q2 so I need to establish a reasonable proxy. I use the “Net percentage of domestic banks tightening standards for C&I loans to small firms” data and conduct a correlation analysis. The correlation analysis shows a highly significant positive relationship with a Pearson coefficient of 0.92643. I can use the C&I loans series in lieu of the missing data, however to address concerns related to discontinuity of the data instead of utilizing the C&I data series for the missing months, I will utilize the series for the full period instead of the Prime Mortgage Loan Standard series.

The next set of variables relates to the demographics and their role in the decision process. Several studies show that females are less willing to accept job transfers than their male colleagues unless the female is the primary provider in the family (Stroh (1999),

Markham and Pleck (1986), Brett and Stroh (1995)). Overall, female rates of mobility differ from male rates so it is important to control for the gender factor.

Family status is another important determinant of whether an employee is willing to relocate. Family residence or lack of support can definitely lead to unsuccessful relocation or job transfers. There are many studies done on this topic with mixed results.

Results show that family resistance and support play a significant role in the decision process (Brett and Reilly (1988) while other studies (Noe, Steffy and Barber(1988)) show that the spouse willingness to relocate or the marital status of the relocating employee has

36 no significant impact on the decision process. This study will include marital status to capture the contribution to the decision making process.

To control for the specific characteristics of corporate relocation I also introduce two additional control variables. Type of Homesale Program (HSTYPE) may decrease the risk to the employee. If the employee is offered a Guaranteed purchase homesale program then their risk of not selling the house is eliminated. Kirschenbaum (1991) examines the intent of an Israeli multi-plant corporation to move. He determines that the availability of housing assistance is one of the key decision making factors of the employee. He further states that the decision to relocate is more of a residential than job related decision.

In addition, homesale benefits such as reimbursing loss on sale or offering a

Homesale bonus not only decrease the risk of loss and eliminate some of the financial burden of selling a home in a depressed housing market but also provide an incentive to the employee to agree on a realistic listing price and minimize the loss aversion. BENFT is a categorical variable, which takes a value of 1 when such benefits are being reimbursed or paid to the employee and 0 if there are no such benefits. I expect the relationship to be positive.

CHAPTER VII

DATA AND SAMPLING PROCESS

This study uses private homesale relocation transactional data for the period of

2004-2014. Generally the authorizations are sent to the RMC once an offer is extended to the employee and he or she is ready to start the process. As previously discussed in the section related to the relocation process, the RMC will start the process of administering the program benefits. Depending on the homesale program offered as part of the policy associated with a particular move, the RMC will start either the listing process or the listing and appraisal process simultaneously.

I start by splitting the data set into two main categories- employees who have accepted the relocation and decided to continue with the process, and employees who have rejected the company offer after starting the homesale process. I continue the sampling process by designating the type of homesale program the employee is entitled to. I believe that any type of guaranteed offer program reduces the risk to the employees of not being able to sell their home at fair value. The second phase in the sampling process is to review each observation to ensure that there is no missing data. Any moves with missing data pertinent to our study are excluded from the sample. The last step is to create the sample with demographic data. Only certain demographic data is required to complete a relocation

38 move, data such as age, education and salary are optional so I limit the demographic data to two main factors- marital status and gender.

The original sample size of all homesale transactions from 2001 to Q4 2014 is

63,690 accepted transfer and 2686 rejected the transfer. Because of very small sample size and data integrity issues, I drop the years 2001- 2004 which does not result in a significant loss of observations. The total final sample size is 58,865 accepted transfers and 2,230 rejected transfers for the period 2004:Q1 to 2014:Q4. I then incorporate the demographic factors in the population and drop any observations for which I do not have marital status and gender. The final sample size with demographics for 2004:Q1 to 2014:Q4 is 39,262 and 1,366 for the accepted and rejected respectively. Then I incorporate the destination factors- our sample size is 38,841 accepted and 1831 rejected and it covers the period of

2005:Q1- 2011: Q4.

The 2012- 2013 Current Population Survey (CPS) shows that between 2012 and

2013, 35.9 million people 1 year and older have relocated to different locations. Figure 3 outlines the reasons for moving. According to the survey, 19.4 percent of the moves are job related which translates into 6.96 million people relocating due to employment reasons.

The 2013 Worldwide ERC U.S. Transfer Volume & Cost Survey quotes that Fortune 500 companies moved 244,595 employees within the U.S. as part of their relocation corporate programs. If we relate the information from both studies, the relocating volume of 244,595 corporate transfers constitutes 3.51 percent of the 6.96 million job related moves. The data set used in these studies is compiled from the moves of one of the relocation companies, which has 22 percent market share of the relocation corporate transfer market. It is also important to note that the relocating employees participate in the same housing market as

39 all of the moves regardless of what the reason is, thus there are exposed to the same market forces as the rest of the migrating population.

Figure 3. Reasons for Moving

Additionally, I explore how the data is represented across the states. It is important to evaluate if the data has a specific concentration in very few states, which can influence the overall interpretation of the results and limit to few housing markets. Figure 3 shows the distribution of observations per departure state. There is a good representation of many states and several of the states such as Texas, Michigan and Illinois had significant declines in housing prices as part of the bubble burst.

Figure 4. HomeSale Transactions by State

The figure below shows the distribution of transaction by state

Table II further examines the rejected relocations per state and as a percentage of the total relocations. It shows the top 20 states by percentage of rejected vs. total. States like Nevada, Hawaii, Florida and DC show higher percentage of rejected relocations. Since most of those states suffered a significant decline and decrease in appreciation rates during the crisis, I can posit that the housing market health and the impact on the house values have put additional pressure on the ability to relocate.

Table II Percentage of Rejected Transfers per State

The table below shows the top 20 states of percentage of rejected transfers to total per departure state

DestState Rejected Accepted Grand Total Percentage Rejected/Total HI 6 82 88 6.82% NV 17 267 284 5.99% WV 11 190 201 5.47% MS 11 195 206 5.34% WY 2 36 38 5.26% DC 22 415 437 5.03% AL 49 1016 1065 4.60% OR 46 978 1024 4.49% FL 125 2676 2801 4.46% NJ 37 796 833 4.44%

WI 69 1490 1559 4.43% GA 127 2759 2886 4.40% VT 1 22 23 4.35% VA 66 1457 1523 4.33% KY 38 847 885 4.29% AZ 60 1346 1406 4.27% CA 172 3895 4067 4.23% SC 28 651 679 4.12% MA 30 718 748 4.01% NM 9 226 235 3.83%

Figure 5 shows the distribution per year of the observations. There is a trend of increasing rejected transfers starting in the second part of 2006 and culminating in the second part of 2007. The maximum number of rejected transactions is registered in August

2007. In addition, the volume of accepted transfers also shows a dramatic decrease after mid-2007. In response to the financial turmoil, many companies placed a freeze or dramatically decreased the volume of their relocation activity as indicated by the ERC survey.

Figure 5. Distribution of Corporate Transfers by Year

The figure below shows the distribution of accepted and rejected corporate transfers per year

When examining the population (Table III), there is a higher percentage of observations with negative equity and equity less than 5 percent of home value in the

43 rejected transfer category- almost double the percentage in the accepted category. The expectation that transferees facing low and negative equity are more inclined to reject a transfer is well illustrated in the LOWEQTY category- 36.73% of rejected transfers have lower than 5 percent equity compared to 17.45% of the accepted relocation transfers.

Homesale program also plays a role- the percentage of transactions with a buyout vs non- buyout in the accepted transfer category is 2:1 while the distribution in the rejected category is almost even.

Table III Sample Size and % of Independent Variables to Rejected and Accepted Transfers

The table below shows the percentage of observations in each of the subgroups as related to the dependent variable of reject or accept for the overall sample size of 61095 observations

LOWEQT HSTYPE

NEGETY NEGETY LOWEQTY Y HSTYPE =0

Overall =1 =0 =1 =0 =1 (No

(Yes) (No) (<5% EQTY) (>5%EQT (Buyout Program) Buyout

(N = 61095) Y) Program)

TFR (%)

1059 2230 421 1809( 819 1411 1171 Rejected (47.49 (3.65%) (18.88%) 81.12%) (36.73%) (63.27%) (52.51%) (=0) %)

Accepted 58865 6221 52644 10269 48596 38859 20006

(=1) (96.35%) (10.57%) (89.43%) (17.45%) (82.55%) (66.01%) (33.99)

HSTYPE

Buyout 40030 4241 35789 6865 33165

( =1) (65.52%) (10.59%) (89.41%) (17.15%) (82.85%)

Unbuyout 21065 2401 18664 4223 16842

(=0) (34.47%) (11.40%) (88.60%) (20.05%) (79.95%)

When reviewing the population with demographic data (Table IV), it is that having additional homesale benefits helps alleviate the financial burden and allows accepting a transfer even if the transferee is facing a negative equity situation. Less than 1 percent of the rejected transfers also have benefits to cover their loss on sale. Single transferees show higher percentage of negative equity situations than married transferees and female transferees are slightly more inclined to face a negative equity situation than their male counterparts.

Table IV Sample Size and % of Demographic Variables

The table below shows the percentage of observations in each of the subgroups as related to the dependent variable of reject or accept as well as within the independent variables if negative equity and under 5% equity of home value.

LOWEQ NEGETY

TFR TFR TY LOWEQTY NEGETY Overall =0(No) =1 (Accept) =0 (Reject) =1 (<5% =0(>5%EQTY) =1(Yes)

(N = 40628) EQTY)

BENFT%

9163 9077 86 2573 6590 1890 7223 Loss on Sale/Bonus (22.55%) (99.06%) (0.94%) (28.08%) (71.92%) (20.63%) (79.37%)

31465 30185 1280 4964 26501 2715 28750 No benefit provided (77.45%) (95.61%) (4.39%) (15.78%) (84.22%) (8.63%) (91.37%)

FMLY

35116 34005 1111 6303 28813 3862 31254 Married/Partner (86.43%) (96.83%) (3.17%) (17.95%) (82.05%) (11.00%) (89.00)

5512 5257 255 123 4278 743 4769 Single (13.57%) (95.37%) (4.63%) 4(22.39%) (77.62%) (13.48%) (86.52%)

GENDER

32920 31822 1098 5983 26937 3623 29297 Male (81.03%) (96.66%) (3.34%) (18.17%) (81.83%) (11.01%) (88.99%)

7708 7440 268 1554 6154 982( 6726 Female (18.97%) (96.52%) (3.48%) (20.16%) (79.84%) 12.74%) (87.26%)

CHAPTER VIII

EMPIRICAL RESULTS

8.1 Descriptive Statistics

Descriptive statistics of the respective series are outlined in Table V and Table VI.

There is significant multicollinearity between certain series and their lags so as part of the modelling I will pay significant attention and retest the final models for multicollinearity by examining VIF and Condition Index. Threshold for VIF is 10 and for Condition Index is 30. From a condition index perspective- FLOW, AFFORD and CSHI show some multicollinearity issues. I further review the correlations in Table VII. All correlations are statistically significant. One possible explanation is the large sample size. The correlations are high correlation amongst CSHI, AFFORD and FLOW.

Table V Summary Statistics

Variable Label N Mean Std Dev Condition Min Max VIF Index TFR 61095 0.96 0.19 0 1 NEGETY 61095 0.11 0.31 0 1 2.24 1.82 LOWEQTY 61095 0.18 0.38 0 1 2.25 2.33 HSTYPE 61095 0.66 0.47 0 1 1.02 3.61 LENDSTND 61095 1.74 21.43 -24.10 75.05 2.72 5.01 FLOW 61095 6.05 5.82 -3.90 14.30 9.19 129.75 AFFORD 61095 138.29 30.81 102.10 207.47 17.56 13.98 UNEMPLOY 61095 6.11 1.72 4.40 10.00 6.77 4.31 CSHI 61095 174.66 23.48 134.45 206.36 10.43 30.72

Table VI Summary Statistics – Expanded Model

Variable Label N Mean Std Dev Condition Min Max VIF Index TFR 40628 0.97 0.18 0 1 NEGETY 40628 0.11 0.32 0 1 2.31 2.28 LOWEQTY 40628 0.19 0.39 0 1 2.30 2.85

HSTYPE 40628 0.63 0.48 0 1 1.10 2.71 LENDSTND 40628 2.67 21.46 -24.10 75.50 2.69 5.28 FLOW 40628 5.14 5.63 -3.90 14.30 8.33 15.50 AFFORD 40628 141.18 31.80 102.10 207.47 19.11 6.20 UNEMPLOY 40628 6.22 1.78 4.40 10.00 7.40 3.26 CSHI 40628 174.57 23.94 134.45 206.36 14.61 6.81 BENFT 40628 0.23 0.42 0 1 1.19 18.33 FMLY 40628 0.86 0.34 0 1 1.70 37.78 GEN 40628 0.81 0.39 0 1 4.60 176.90 MALE_FMLY 40628 0.73 0.44 0 1 1.72 12.69 FML_FMLY 40628 0.13 0.34 0 1 4.43 4.16

Table VII Correlation Matrix

The correlation matrix below shows the Pearson correlation coefficients for the sample size of 61095 observations

LOWEQT UNEMPLO LENDSTN TFR NEGETY HSTYPE AFFORD FLOW CSHI Y Y D

TFR 1

- NEGETY 0.0500 1 *

- 0.0532 HSTYPE 0.0357 1 * *

- - 0.7417 LOWEQTY 0.0938 0.0357 1 * * *

- UNEMPLO 0.0117 0.2229 0.2122 0.1037 1 Y * * * *

- 0.0289 0.2178 0.2066 AFFORD 0.1174 0.8777* 1 * * * *

- - - 0.0167 0.1025 - FLOW 0.2234 0.2207 0.8083 1 * * 0.7273* * * *

- - - - 0.0942 - 0.6285 CSHI 0.0303 0.1966 0.1864 0.8845 1 * 0.8885* * * * * *

- - - - LENDSTN 0.0943 0.1021 0.0632 0.0131 0.0513* 0.0007 0.4052 0.0604 1 D * * * * * * Note: Statistical significance at p < .05 is marked with *

8.2 Empirical Results

In the first model, I test the hypothesis that the negative equity and low equity provide financial constraints that limit the ability of the transferee to relocate. Table VIII shows the results of the stepwise logistics regression and the parameters estimates. Both models with negative equity or low equity show statistically significant impact with the expected negative sign. In addition, type of homesale is also a significant factor. The buyout program has a positive impact on the acceptance since it eliminates the risk of suffering a large loss on sale or not being able to sell the existing home. It is safe to conclude that the empirical results support our hypothesis that negative and low equity have a negative impact on the willingness to accept a corporate transfer when controlling for departure state variability and year.

Table VIII Empirical Results Model 1

The results of the logit analysis are shown below. The dependent variable is TFR where accepted transfer equals 1 and rejected transfer equals 0. Model 1A includes NEGETY as independent variable while Model1B includes the categorical variable LOWEQTY. Model 1 tests the hypothesis that the probability of accepting a corporate transfer is negatively affected by the negative equity of the homeowner. Both models control for departure state and year.

Variable Expected Model 1A Model 1B Sign

Intercept 1.6855 *** (12343.03) HSTYPE + 0.2479*** ( 164.28) NEGETY - -0.3055***(134.72) Pseudo R2 0.015

Intercept 1.7624***(11994.77) HSTYPE + 0.2376*** (148.02) LOWEQTY - -0.4519***(453.26) Pseudo R2 0.031 Controls for Year and Departure State Number Obs 61095 61095 Note: Wald Chi-Square statistic is shown in parentheses. Theoretical expectations are noted by +/-/ or m for mixed. Statistical significance at 10%, 5%, and 1% levels is indicated by * , **, and *** , respectively.

The next set of models addresses the second hypothesis that market conditions have a deciding factor in one’s ability to relocate. As indicated in the recent ERC survey,

48 distressed market and falling prices are the top reasons why transferees will reject a transfer. I posit that the probability of accepting a transfer is positively related to affordability(AFFORD), liquidity as proxied by the Feds Flow of Funds(FLOW) and a more favorable housing market –rising S&P500/ Case Shiller 20-city Index (CSHI). Higher affordability, higher liquidity and rising housing prices equate to higher probability of disposing the current home and finding an affordable new home in the new location that can be financed because of the higher liquidity. In addition, I expect that the homesale type

(HSTYPE=1) also has a positive effect. If the employer is offering a guaranteed homesale program, the risk of not being able to dispose of the existing home in the old location is eliminated since the employer guarantees the purchase of the home from the transferee after some period of marketing.

On the other hand, the higher unemployment rates as well as facing negative equity situation (NEGETY=1) have a negative impact on the acceptance rate of transfers, so I expect negative coefficients. Similarly, higher net percentage of banks tightening their standards when issuing prime mortgages also has a negative effect on the decision to relocate. Tight standards not only mean that the transferee may face difficulties obtaining a new mortgage but also that buyers will face similar challenges, thus diminishing the pool of possible buyers and the ability to sell the home faster and at an acceptable price.

The results of the logistic stepwise regression are displayed in Table IX. Most of the variables are highly statistically significant. It is interesting to note the signs of the

S&P500/ Case Shiller 20-city Index. The expectations were of mixed relationship. The contemporaneous moving average of the S&P500/ Case Shiller 20-city Index has a negative impact on the decision to relocate similarly to the two months lag while the one-

49 month lag has a positive impact. The rising prices have dueling impacts on the decision to move. On one hand, rising prices mean that the transferees could list and sell their home for a higher price, but it means facing rising prices in the new location. As cited by the

ERC survey, employers expect transferees to start in the new location within four weeks of accepting the offer and usually allow on average two weeks of accepting which means that they need to start looking to buy a house and close on a house within month two of receiving an offer. Therefore, rising prices in the two months prior to moving has a negative impact on the decision to move and buy a new house in the new location. Since most of the transferees moving are male and married, unemployment also matters in the decision since one of the spouses may need to find a new job in the new location. The effect of unemployment is as expected negative but it comes with a two-month lag.

Overall, the results of the logit analysis show that market determinants combined with negative equity and low equity have a significant impact on the decision to relocate.

Table IX Empirical Results Model 2

The results of the logit analysis are shown below. The dependent variable is TFR where accepted transfer equals 1 and rejected transfer equals 0. Model 2A includes NEGETY as independent variable while Model2B includes the categorical variable LOWEQTY. Model 2 tests the hypothesis that the efficiency and affordability of the housing market affect the probability of accepting a corporate relocation transfer. The logit modelling includes stepwise and backward selection. The variables that have been dropped as part of the selection process do not show parameter estimates since the modeling procedure does not provide estimates for eliminated variables

Variable Expected Model 2A Model 2B Sign Intercept 1.2200 (1.23) HSTYPE+ + 0.6315 *** (195.82) NEGETY- - -0.6851***(135.88) UNEMPLOY - -- UNEMPLOY_LAG1 - -- UNEMPLOY_LAG2 - -0.2856***(56.89) LENDSTND - -- LENDSTND_LAG1 - 0.0304***(13.59) LENDSTND_LAG2 - -0.0310***(15.96) FLOW + 0.0378***(11.17) FLOW_LAG1 + -- FLOW_LAG2 + -- CSHI ? -- CSHI_LAG1 ? 0.1935***(65.41) CSHI_LAG2 ? -0.1977***(77.58) AFFORD + 0.0294***(79.06) AFFORD_LAG1 + -- AFFORD_LAG2 + --

Pseudo R2 0.044

Intercept 1.3683 (1.54) HSTYPE 0.5930*** (180.83) UNDER -1.0118***(452.24) UNEMPLOY -- UNEMPLOY_LAG1 -- UNEMPLOY_LAG2 -0.2700***(50.28) LENDSTND -- LENDSTND_LAG1 0.0306***(13.65) LENDSTND_LAG2 0.0127***(16.02) FLOW 0.0356**(9.80) FLOW_LAG1 -- FLOW_LAG2 -- CSHI -- CSHI_LAG1 0.1779***(55.07) CSHI_LAG2 -0.1824***(65.72) AFFORD 0.0293***(78.37) AFFROD_LAG1 -- AFFORD_LAG2 -- Pseudo R2 0.060 Controls for Year and Departure State Number Obs 61095 61095 Note: Wald Chi-Square statistic is shown in parentheses. Theoretical expectations are noted by +/-/ or m for mixed. Statistical significance at 10%, 5%, and 1% levels is indicated by * , **, and *** , respectively.

To ensure that the results related to LOWEQTY are not sensitive to the threshold selections, I perform further analysis on two other threshold levels. I introduce two other variables related to LOWEQTY. I set the threshold at 7.5 percent, which is the average costs of sale of the overall data set. Costs of sale include real estate commission to the broker, closing costs such as title work, recording etc. and any concessions to buyers such as repairs. Additionally, I introduce a second threshold level of 10 percent. The results of

Model 2 incorporating the two new threshold levels are presented in Table X.

The result form the robustness analysis on the selected threshold show no significant differences from the main model. All of the signs and statistical significance of the variables stays the same. The parameter estimates of the LOWEQTY variable also do not show any significant changes in magnitude. The estimate for LOWEQTY at 5 percent is -1.0118 while the parameter estimates for LOWEQTY at 7.5 percent and LOWEQTY at

10 percent are -1.0556 and -1.0050 respectively. Additionally, the pseudo R2 of all three models – Model 2B, 2C and 2D- are very close: 0.060, 0.064 and 0.063 respectively. Thus,

51 it can be concluded that the model is not sensitive to threshold selection of the LOWEQTY variable.

Table X. Empirical Results Model 2- Robustness Check

The results of the logit analysis are shown below. The dependent variable is TFR where accepted transfer equals 1 and rejected transfer equals 0. Model 2C includes LOWEQTY ate 7.5 percent threshold as independent variable while Model2D includes LOWEQTY at 10 percent level . Model 2 tests the hypothesis that the efficiency and affordability of the housing market affect the probability of accepting a corporate relocation transfer. The logit modelling includes stepwise and backward selection. The variables that have been dropped as part of the selection process do not show parameter estimates since the modeling procedure does not provide estimates for eliminated variables

Variable Expected Model 2C Model 2D Sign LOWEQTY (7.5%) LOWEQTY (10%) Intercept 1.4019 (1.61) 1.4950 (1.84) HSTYPE + 0.5895 *** (178.03) 0.5833***(174.33) LOWEQTY (7.5%) - -1.0556***(528.50) -1.0050***(494.31) UNEMPLOY - -- -- UNEMPLOY_LAG1 - -- -- UNEMPLOY_LAG2 - -0.26699***(50.05) -0.2732***(51.37) LENDSTND - -- -- LENDSTND_LAG1 - 0.0302***(13.30) 0.0298***(12.90) LENDSTND_LAG2 - -0.0310***(15.73) -0.0306***(15.34) FLOW + 0.0359***(9.92) 0.0354***(9.68) FLOW_LAG1 + -- -- FLOW_LAG2 + -- -- CSHI ? -- -- CSHI_LAG1 ? 0.1740***(52.43) 0.1758***(53.68) CSHI_LAG2 ? -0.1784***(62.64) -0.1803***(64.14) AFFORD + 0.0296***(79.62) 0.0294***(78.65) AFFORD_LAG1 + -- -- AFFORD_LAG2 + -- -- Pseudo R2 0.064 0.063

Controls for Year and Departure State Number Obs 61095 61095 Note: Wald Chi-Square statistic is shown in parentheses. Theoretical expectations are noted by +/-/ or m for mixed. Statistical significance at 10%, 5%, and 1% levels is indicated by * , **, and *** , respectively.

In the next two regression analyses, I expand Model 1 and Model 2 and introduce the demographic factors of gender and marital status as well as the additional homesale benefits of loss on sale reimbursement and homesale bonus to examine their impact on corporate mobility. The results of the tests are displayed in Tables XI and XII. I also introduce two interaction variables to further explore the significance of marital status and gender. As the most recent Current Population Survey shows, there is a shift in the mobility amongst gender and marital status. The percentages of single migrants and female migrants

52 have increased. Results from Model 3 stepwise logit regression show that gender and family status are not significant while the interactions are (TableXI). Being male and married and being female and married are statistically significant to the decision to relocate and exhibit positive impact.

The last model is an expanded version of Model 2 incorporating the additional demographic variables. The results are very similar to the original empirical testing of model 2 and model 3 with the difference of significance related to the Affordability Index and the Case Shiller and Lending Standards. In the last model, contemporaneous Case

Shiller Index is insignificant. Similarly, Lending standards are no longer a factor since none of the three variables related to tightening standards are significant. At the same time current month’s affordability becomes a factor. Overall, the expanded model supports the expectations that gender and family status play a role in mobility in addition to the market factors.

Table XI Empirical Results Model 3

The results of the logit analysis are shown below. The dependent variable is TFR where accepted transfer equals 1 and rejected transfer equals 0. Model 3A includes NEGETY as independent variable while Model3B includes the categorical variable LOWEQTY. Model 3 expands on Model 1 in testing the hypothesis that negative equity increases the probability of rejecting a transfer by introducing demographic factors into the model. Both models control for departure state and year. The logit modelling includes stepwise and backward selection. The variables that have been dropped as part of the selection process do not show parameter estimates since the modeling procedure does not provide estimates for eliminated variables

Variable Expected Sign Model 3A Model 3B

Intercept 1.5688 **(2228.92) HSTYPE + 0.1381 *** ( 30.76) NEGETY- - -0.3246***(85.46) BENFT + 0.6220***(203.24) GENDER + -- FMLY - -- MALE*FMLY ? 0.1605***(23.46) FMLE*FMLY ? 0.1645**(12.91) Pseudo R2 0.035

Intercept 1.6480***(2315.47) HSTYPE 0.1231*** (24.03) LOWEQTY -0.4376***(243.51)

BENFT 0.6519***(220.13) GENDER -- FMLY -- MALE*FMLY 0.1446***(18.68) FMLE*FMLY 0.1519**(10.82) Pseudo R2 0.047 Controls for Year and Departure State Number Obs 40628 40628 Note: Wald Chi-Square statistic is shown in parentheses. Theoretical expectations are noted by +/-/ or m for mixed. Statistical significance at 10%, 5%, and 1% levels is indicated by * , **, and *** , respectively.

Table XII Empirical Results Model 4

The results of the logit analysis are shown below. The dependent variable is TFR where accepted transfer equals 1 and rejected transfer equals 0. Model 4A includes NEGETY as independent variable while Model4B includes the categorical variable LOWEQTY. Model 4 expands on the testing the hypothesis that the efficiency and affordability of the housing market affect the probability of accepting a corporate relocation transfer by introducing demographic factors into the model. Both models control for departure state and year. The variables that have been dropped as part of the selection process do not show parameter estimates since the modeling procedure does not provide estimates for eliminated variables

Variable Expected Sign Model 4A Model 4B Intercept 1.0424 (1.21) HSTYPE + 0.3833 ***(45.41) NEGETY - -0.6816***(77.31) UNEMPLOY - -- UNEMPLOY_LAG1 - -- UNEMPLOY_LAG2 - -0.3350***(67.38) LENDSTND - -- LENDSTND_LAG1 - -- LENDSTND_LAG2 - -- FLOW + -- FLOW_LAG1 + -- FLOW_LAG2 + -- CSHI ? 0.1022***(128.14) CSHI_LAG1 ? -- CSHI_LAG2 ? -0.1010***(136.47) AFFORD + 0.0258***(81.51) AFFORD_LAG1 + -- AFFORD_LAG2 + -- BENFT+ + 1.4011***(145.80) FMLY - -- GENDER + -- MALE*FMLY ? 0.3327***(20.78) FML*FMLY ? 0.3254**(9.98) Pseudo R2 0.052

Intercept 1.3020 (1.8819) HSTYPE 0.3548*** (38.64) LOWEQTY -0.9393***(230.35) UNEMPLOY -- UNEMPLOY_LAG1 -- UNEMPLOY_LAG2 -0.3229***(62.28) LENDSTND -- LENDSTND_LAG1 -- LENDSTND_LAG2 -- FLOW -- FLOW_LAG1 -- FLOW_LAG2 -- CSHI 0.0939***(107.24) CSHI_LAG1 -- CSHI_LAG2 -0.0933***(115.73) AFFORD 0.0256***(79.91) AFFROD_LAG1 -- AFFORD_LAG2 -- BNFT 1.4300***(151.42)

FMLY -- GENDER -- MALE*FMLY 0.2960***(16.28) FML*FMLY 0.2971**(8.26) Pseudo R2 0.064 Controls for Year and Departure State Number Obs 40628 40628 Note: Wald Chi-Square statistic is shown in parentheses. Theoretical expectations are noted by +/-/ or m for mixed. Statistical significance at 10%, 5%, and 1% levels is indicated by * , **, and *** , respectively.

8.3 Destination factors impact on willingness to relocate

In order to evaluate the destination factors that can influence the decision to relocate, I assess four new categories of factors- crime, education, state employment rate and state house purchase price. Thus, I introduce several new variables related to these main categories by constructing the difference between the destination state and departure state to examine how those differences affect the willingness to relocate.

The Uniform Crime Reporting Statistics is published annually by the FBI and it represents data on national and state level of violent crimes and property crimes. “The

FBI’s Uniform Crime Reporting (UCR) Program is a nationwide, cooperative statistical effort of nearly 18,000 city, university and college, county, state, tribal, and federal law enforcement agencies voluntarily reporting data on crimes brought to their attention.”

8There are various different subcategories of crimes under the two main components. I focus on three types of crime- violent crimes, burglary and robbery rates as reported in the

UCR tables. The data is reported as number of crimes per 100,000 population. Since the data is reported annually and the model utilizes monthly frequency, I interpolate the data to higher frequency by using the Expand function in SAS which calculates the values by applying a cubic spline function to the input time series data with lower frequency. I posit

8 Information on the program and data collected from http://www.ucrdatatool.gov/abouttheucr.cfm

55 that the states with higher crime rates will affect the decision to relocate negatively. I define three variables for each of the crimes- violent crime, robbery and burglary- VIOL_DIFF,

ROB_DIFF and BULG_DIFF. Burglary is defined as a crime against property while violent crimes and robbery are against an individual. The variables are constructed by taking the difference between the monthly value of the destination state vs. the departure state value.

Similarly, I determine a proxy for assessing the level of education and school system. States with better school systems will provide a positive impact on the decision to relocate. I take the annual state spent per pupil as proxy for education levels as reported in the Annual Survey of Local Government Finances - School Systems by the U.S. Census

Bureau. I posit that the higher the spent per pupil, the better the levels of education and school system are in the state. Since those data points are available in annual frequency, I apply the same methodology as the crime data to convert to monthly data points to match the frequency of the dataset. I construct a variable EDU_DIFF as the difference between the interpolated monthly spent per pupil in the destination state vs departure state.

I also include the monthly unemployment rate by state as reported by the Bureau of Labor Statistics and the monthly Housing Purchase Index per state as published by the

Federal Housing Finance Agency. I construct the difference between the monthly values of the destination and departure states- UNEMPLOY_DIFF and HPI_DIFF respectively.

In Table XIII, I present the variables summary statistics. The data covers the period from 2005: Q1 to 2011: Q4 due to availability of data related to some of the destination factors. I pay particular attention to the value inflation factor (VIF) and Conditional Index to examine multicollinearity. Additionally, following Belsely, Kuh, and Welsch (1980)

56 methodology as related to examining collinearity, I also review the variance decomposition proportions for each of the Condition Index higher than 30. The threshold for large variance decomposition proportions as outlined by the three authors is 50 percent. If a condition index higher than 30 is associated with two or more variables with variance decomposition proportions higher than 50 percent, these variables may be causing collinearity problems.

I indicate such variables with ** in the table below. Further examination of the variance decomposition proportions show that UNEMPLOY_DIFF has large proportions with

Affordability (AFFORD) and Case Shiller Index (CSHI). Education also has large proportion variance with national level of unemployment (UNEMPLOY).

Table XIII Summary Statistics – Model 5

Variable Label N Mean Std Dev Condition Min Max VIF Index TFR 40672 0.96 0.21 0 1 NEGETY 40672 0.11 0.32 0 1 2.24 1.62 LOWEQTY 40672 0.19 0.39 0 1 2.23 1.84 HSTYPE 40672 0.66 0.47 0 1 1.01 2.27 LENDSTND 40672 6.87 23.76 -24.10 75.50 3.21 2.38 FLOW 40672 6.31 5.50 -3.90 14.20 7.14 4.08 AFFORD 40672 130.11 27.62 102.10 199.23 24.96 3.18 UNEMPLOY 40672 5.95 1.93 4.40 10.00 13.34 1.92 CSHI 40672 181.29 24.21 138.28 206.36 24.11 4.28 VIOL_DIFF 40672 8.70 195.50 -1168.08 1377.90 2.26 4.38 ROB_DIFF 40672 4.95 92.53 -1197.48 736.73 1.97 4.68 BULG_DIFF 40672 17.47 285.98 -874.89 909.75 2.23 5.08 HPI_DIFF 40672 0.59 45.57 -203.83 205.40 1.41 16.57 EDU_DIFF 40672 -63.70 2581.22 -12644.88 12970.72 1.75 42.41** UNEMPLOY_DIFF 40672 0.01 1.46 -7.70 8.80 1.29 178.41**

I start the modelling by including all the housing market variables as well as the newly constructed destination factors variables. The preliminary results show that violent crimes variable (VIOL_DIFF) is not significant. I evaluate the correlation between each of the variables and present the results in Table XIV. I find that there is a high statistically

57 significant correlation between VIOL_DIFF and ROB_DIFF of 0.6553.9 I decide to drop the variable from the model. I further evaluate the model and determine that the Housing price index ( HPI_DIFF) is not significant either. One explanation could be that the data is specific to relocation and the employers provide support in regards to purchasing properties in the new location. Since the home sale cost and the closing cost of the new purchase are generally covered by policy and the compensation is adjusted to the new cost of living, the impact of the housing prices may not be as an important factor as are the overall health of the market and other destination factors. Additionally, the relocating employees would probably like to move to areas of healthy house markets and increasing prices. There is a high frequency of multiple relocations, so higher purchase prices will mean that the property will sell at a premium in the next relocation move. I re-run the probit model after dropping the VIOL_DIFF and HPI_DIFF variables. Table XV displays the results of model

5B which includes the LOWEQTY vs NEGEQTY as a main independent variable. The results for the other model are available upon request. They are very similar in size of parameters and no differences in statistical significance between the two models, so I display the model with higher Pseudo R2.

9 Results are not displayed but available upon request

Table XIV Correlation Model

The correlation matrix below shows the Pearson correlation coefficients for the sample size of 61095 observations

BULG_DI UNEMPLOY_DIF VIOL_DIFF ROB_DIFF HPI_DIFF EDU_DIFF FF F

VIOL_DIFF 1

ROB_DIFF 0.6553* 1

BULG_DIFF 0.3997* 0.1613* 1

- HPI_DIFF 0.1362* 0.1054* 1 0.3219*

- EDU_DIFF -0.0789* 0.1799* 0.1820* 1 0.5889*

UNEMPLOY_DIF 0.2339* 0.2239* 0.2710* -0.3510* -0.0772* 1 F

Note: Statistical significance at p < .05 is marked with *

Table XV Empirical Results Model 5

The results of the logit analysis are shown below. The dependent variable is TFR. Model 5B includes the categorical variable LOWEQTY. Model 5 expands on the testing the hypothesis that the efficiency and affordability of the housing market affect the probability of accepting a corporate relocation transfer by introducing differences between the destination and departure key factors into the model. The model controls for year. The variables that have not been dropped as part of the selection process do not show parameter estimates

Variable Expected Model 5B Sign Intercept -1.25 (1.19) HSTYPE+ + -0.3103** (185.75) LOWEQTY- - -0.4170** (260.87) UNEMPLOY - -- UNEMPLOY_LAG1 - -- UNEMPLOY_LAG2 - -0.2779*(8.52) LENDSTND - -- LENDSTND_LAG1 - -- LENDSTND_LAG2 - -0.0149*(3.72) FLOW + -- FLOW_LAG1 + -- FLOW_LAG2 + -- CSHI ? -- CSHI_LAG1 ? -- CSHI_LAG2 ? -0.1326*(10.16) AFFORD + 0.0253*(7.65) AFFORD_LAG1 + -- AFFORD_LAG2 + -- VIOL_DIFF - --

ROB_DIFF - -0.0003*(4.77) BULG_DIFF - 0.0001*(4.73) EDU_DIFF + 0.0001*(4.00) HPI_DIFF ? -- UNEMPLOY_DIFF - 0.0134 (2.76)

Pseudo R2 0.049 Number of OBS 40672 Note: Wald Chi-Square statistic is shown in parentheses. Theoretical expectations are noted by +/-/ or ? for unsure. Statistical significance at 5%, and 1% levels is indicated by * , and **, respectively.

The results are similar with the previous models as related to the significance of the individual housing factors related to unemployment, affordability and lending standards. I see some differences in regards to the Case Shiller Index and the Flow of funds. I see that

Case Shiller Index is only significant in regards to two months lag and the flow of funds is not significant at all. This could be a result of some of the collinearity issue I presented earlier.

When I focus on the destination factor variables, I see that education proxy and robbery crime rates show significance at the 5% level and exhibit the expected signs.

Burglary on the other hand is significant but shows an opposite sign. One possible explanation could be that the sample population is not as concerned about property crimes since generally the relocation employees are required to follow certain standards as laid out by the employer to qualify for the home purchase benefits. Many times the relocation management company has to review and approve the new home purchase to ensure that the property will be able to sell in case of a subsequent relocation. Additionally, the utilized data is on state level. More accurate estimation can be provided if I utilize zip code data to truly determine the difference between the destination and departure areas. However, the data set does not capture the zip code or address of the new destination home address to be able to determine the difference on the more granular level. Thus, the state level analysis provides a proxy evaluation of how destination factors affect the willingness to relocate. In

60 regards to state unemployment rate, the estimate is insignificant at the 5% level and also displays the incorrect sign which could be a result of the collinearity issues I discovered when analyzing the variance proportions.

Overall, the results confirm the previous studies’ findings that destination factors do have an impact in the decision to move and need to be considered when analyzing the behavior of relocating employees and their willingness to relocate. Career opportunities and income considerations were not included in the model due to limitations of the data set. Incremental salary and incremental career opportunities are also important factors in the willingness to relocate and need to be considered in the overall decision making process. However, the data set does not contain the previous position and salary of the relocating employee to include in the model.

CHAPTER IX

CONCLUSION

This study aims to continue the research related to mobility by focusing on corporate relocations and the effects of the housing market on the decision to move. I utilize private relocation data of home sale transactions for the period of 2004-2014 to examine the effects of equity constraints and housing market dynamics on the decision to relocate.

Many factors affect the ability to relocate. Previous studies have examined various bio- demographic and economic factors. The study focuses on the equity constraints that home owners face and how declining market conditions can contribute to their inability to move.

The stepwise logit regression results show that relocating employees facing negative equity situations and equity less than 5% of home value have a greater chance of rejecting a relocation transfer decreasing their mobility. Housing market health and level of affordability also have a significant effect on the ability of employees to relocate.

Additionally, the homesale programs offered by the employer as part of the relocation package also play a role in the willingness of employees to relocate. Similarly, destination factors such as education and crime rate also have an impact on the decision to accept a corporate transfer.

It is important for employers to understand the factors driving the decision to relocate so that they can structure relocation programs that can entice the employees to

62 accept the offer for geographical transfer. The study also expands the research related to the impact of the housing market declines on migration.

In conclusion, this study shows that levels of housing wealth proxied by low or negative equity are an important factor in explaining mobility and the willingness of employees to accept transfers.

BIBLIOGRAPHY

Abraham, J. M. and P. H. Hendershott. "Bubbles in Metropolitan Housing Markets." Journal of Housing Research 7.2 (1996): 191-206.

Allard, C. "Changing times: Former facst-trackers take a watching the clock." Canadian Business 63 (1990): 163.

Bartel, Ann P. "The Migration Decision: What Role Does Job Mobility Play?" The Amercian Economic Review 69.5 (1979): 775-786.

Belsley, D.A., E. Kuh and R. E. Welsch. Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. New York: John Wiley & Sons, 1980.

Brett, J. M and L. K. Stroh. "Willingness to Relocate Internationally." Human Resource Management Journal 34.3 (1995): 405-424.

Brett, Jeanne M. and Anne H. Reilly. "On the Road Again: Predicting the Job Transfer Decision." (n.d.).

Case, K. and R. Shiller. "The Efficiency of the Market for Single Family Homes." American Economic Review 79 (1989): 125-137.

Chan, Sewin. "Spatial Lock-in: Do Falling Prices Constrain Residential Mobility?" Journal of Urban Economics 49.3 (2001): 567-586.

Dieleman, F. M., W. A.V. Clark and M. C. Deurloo. "The Geography of Residential Turnover in 27 large US Metropolitan Housing Markets 1985-1995." Urban Studies 37.2 (2000): 223- 245.

DiPasquale, D and W. C. Wheaton. "Urban Economics and Real Estate Markets." Englewood Cliffs (1996).

Eby, Lillian T. and Joyce E. Russell. "Predictors of Employee Willingness to Relocate for the Firm." Journal of Vocational Behavior 57 (2000): 42-61.

Engelhardt, Gary V. "Nominal Loss Aversion, housing equity contraints, and household mobility: evidence from the Unites States." Journal of Urban Economics 53 (2003): 171-195.

ERC, Worldwide. "2013 Worldwide ERC U.S. Transfer Volume and Cost Survey." 2013.

Feldman, Daniel C. and Mark C. Bolino. "Moving On Oot: When are Employees Willing to Follow their Organization During Corporate Relocation." Journal of Organizational Behavior 19 (1998): 275-288.

Ferreira, Fernando, Joseph Gyourko and Joseph Tracy. "Housing Busts and Houshold Mobility." Journal of Urban Economics 68 (2010): 34-45.

Genesove, David and Christopher J Mayer. "Equtiy and Time to Sale in the Real Estate Market." The American Economic Review 87.3 (1997): 255-269.

Genesove, David and Christopher Mayer. "Loss Aversion and Seller Behavior: Evidence from the Housing Market." The Quarterly Journal of Economics 116.4 (2001): 1233-1260.

Gould, S and L. E. Penley. "A Study of Correlates of the Willingness to Relocate." Academy of Management Journal 28 (1985): 472-478.

Han, Aaron and Jerry Hausman. "Fexible Parametric Estimation of Duration and Competing Risk Models." Journal of Applied Econometrics 5.1 (1990): 1-28.

Henley, Andrew. "Residential Mobility, Housing Equity and The Labour Market." The Economic Journal 108 (1998): 414-427.

Kirschenbaum, Alan. "The Corporate Transfer: Origin and Destination Factors in the Decision to Change Jobs." Journal of Vocational Behavior 38 (1991): 107-123.

Landau, J. and T. H. Hammer. "Clerical Employees' Perception of Intraorganizational Career Opportunities." Academy of Management Journal 29 (1986): 385-404.

Markham, W. T. and J. H. Pleck. "Sex and Willingness to Move for Occupational Advancement." Sociological Quarterly 27 (1986): 121-143.

Moore, E. G and W. A.V. Clark. Housing and Households in American Cities: Structures and Change in Population Mobility 1974-1982. Dissertation. Wisconsin: The University of Wisconsin, 1990.

Moscarini, Guiseppe and Kaj Thomsson. "Occupational and Job Mobility in the US." The Scandinavian Journal of Economics 109.4 (2007): 807-836.

Noe, Raymond A. and Alison E Barber. "Willingness to accept mobility opportunities: Destination makes a difference." Journal of Organizational Behavior 14 (1993): 159- 175.

Noe, Raymond A., Brian D. Steffy and Alison E. Barber. "An Investigation of the Factors Influencing Employees' Willingness to Accept MObility Opportunities." Personnel Psychology 41 (1988): 550-580.

Schulhofer-Wohl, Sam. "Negative Equity Does Not Reduce Homeowners' Mobility." Federal Reserve Bank of Minneapolis Quarterly Review 35.1 (2012): 2-13.

Strassmann, W. P. "Mobility and Affordability in US Housing." Urban Studies 37.1 (2000): 113- 126.

Stroh, Linda K. "Does Relocation Still Benefit Coprorations and Employees?An Overview of the Literature." Human Resource Management Review 9.3 (1999): 279-308.

Wheaton, W.C. "Vacanyc, Search, and Prices in a Housing Market Matching Model." Journal of Political Economy 98 (1990): 1270-1292.

APPENDIX

Tax Concepts in Relocation - Eleven Key Elements and Procedures of an Amended Value Prepared by Worldwide ERC® Tax Counsel, Peter K. Scott Peter Scott Associates, November 2012

The ``11 Key Elements and Procedures of an Amended Value Option'' were published in

1985 by the ERC Law and Government Relations Committee to create a ``best practice'' document for tax purposes addressing the IRS requirements for a two sale transactions program and by no means establish rigid rules around administering these programs but rather considerations for a compliant program.

1. Any employee ("EMPLOYEE") wishing to take advantage of the Amended Value

Option who lists his/her home with a real estate broker must include a suitable

exclusion clause in the listing agreement whereby the listing agreement is terminated

upon the sale of the home to either the employer or the relocation company.

2. Under no circumstances should EMPLOYEE accept a down payment from any potential buyer.

3. Under no circumstances should EMPLOYEE sign an offer presented by any potential buyer.

4. EMPLOYEE enters into a binding contract ("Contract of Sale") with his/her employer or the relocation service company ("PURCHASER’’).

5. After the execution of the Contract of Sale with PURCHASER and after EMPLOYEE has vacated the home, all of the burdens and benefits of ownership pass to the

PURCHASER.

6. The Contract of Sale between EMPLOYEE and PURCHASER at the higher price is unconditional and not contingent on any event, including the potential buyer obtaining a mortgage commitment.

7. Neither EMPLOYEE nor the employer in the case of a relocation company transaction exercises any discretion over the subsequent sale of the home by the PURCHASER.

8. PURCHASER enters into a separate listing agreement with a real estate broker to assist with the resale of the property.

9. PURCHASER enters into a separate agreement to sell the home to a buyer.

10. PURCHASER arranges for the transfer of title to the buyer.

11. The purchase price eventually paid by the buyer has no effect on the purchase price paid to EMPLOYEE.

ESSAY 2: LOSS AVERSION IN THE HOUSING MARKET

LOSS AVERSION IN THE HOUSING MARKET

SVETOSLAVA ALEXANDROVA

ABSTRACT

Housing markets exhibit some puzzling behavior that cannot be completely explained by rational market dynamics. Standard rational theory posits that sellers and buyers in the housing market focus on current market value and ignore the initial purchase price as sunk costs. Rational market participants act to maximize their wealth ignoring any psychological biases. Several studies have invalidated that notion. Genesove and Mayer

(2001) observe that sellers use the original purchase price as a reference point to determine their prospective losses or gains when determining an asking price. As a result, sellers facing a prospective loss set up higher asking price at a cost of longer time on market.

This study continues the behavioral line of research by examining the loss aversion effects in the U.S. housing market using a private data of home sale transactions as part of corporate relocations for the period of 2003-2014. I expand on the models of Genesove and

Mayer(2001) and Bokhari and Geltner (2011) by introducing a less noisy proxy for equity and property value to determine the prospective losses or gains. I further examine the effect of the crisis on the loss aversion behavior exhibited by the sellers. The results are consistent with Genesove and Mayer’s and Bokhari and Geltner’s findings. I determine that loss aversion has significant effect on the asking price and time on market. The results indicate that a seller who faces a loss will set up an asking price 5.69 percent higher than they would

70 otherwise do. I also observe that loss aversion behavior is much more acute in time of crisis. In regards to time on market, the empirical results show that sellers facing a loss will experience a reduction in the hazard rate of sale resulting in longer time on the market. I introduce new dimension to the models by including income and demographics variables in the analysis. The results show that income, gender and family status increase the loss aversion and time on market.

TABLE OF CONTENTS

Page

ABSTRACT………………………………………………………………………….70

LIST OF TABLES…………………………………………………………………...74

LIST OF FIGURES………………………………………………………………….75

CHAPTER

I. INTRODUCTION………………………………………………...... 76

II. PROSPECT THEORY AND LOSS AVERSION OVERVIEW………….80

III. GENESOVE AND MAYER LOSS AVERSION MODEL……………….84

3.1 Review of Genesove and Mayer Theoretical Model…………………..85

3.2 Genesove and Mayer Empirical Results and Implications…………….88

3.3 Bokhari and Geltner Commercial Real Estate Model………………....89

IV. OVERVIEW OF THE RELOCATION HOMESALE PROCESS………..91

4.1 Home Marketing Process…………………………………………...... 92

V. DATA AND SAMPLING PROCESS…………………………………….95

VI. MODEL AND HYPOTHESES…………………………………………...100

6.1 Theoretical Model……………………………………………………..100

6.2 Hypotheses…………………………………………………………….101

6.3 Methodology…………………………………………………………..102

VII. EMPIRICAL MODELS AND VARIABLES…………………………….103

7.1 Empirical Models…………………………………………………….103

7.2 Variables Construction………………………………………………..104

VIII. EMPIRICAL RESULTS………………………………………………….107

8.1 Descriptive Statistics………………………………………………….107

8.2 Empirical Results………………………………………………….…..109

8.2.1 Loss Aversion Estimation Results………………………….…109

8.2.2 Time on Market Estimation Results…………………………..111

8.2.3 Statistical Significance of the Coefficients……………………113

8.2.4 Demographics Effects on Loss Aversion and Time on Market 115

IX. CONCLUSION…………………………………………………………...118

BIBLIOGRAPHY………………………………………………………………...120

LIST OF TABLES

Table Page Table I. Database Variables ...... 95

Table II. Variable Legend ...... 106

Table III. Descriptive statistics – 2003:Q1- 2014:Q4 ...... 108

Table IV. Descriptive statistics – Pre- Crisis Period 2003:Q1- 2005:Q4 ...... 108

Table V. Descriptive statistics –Crisis Period 2006:Q1- 2009:Q4 ...... 108

Table VI. Descriptive statistics – Post Crisis Period 2010:Q1- 2014:Q4 ...... 108

Table VII. Loss Aversion and Listing Price ...... 111

Table VIII. Marginal Effect of Loss Aversion on Listing Price ...... 111

Table IX. Loss Aversion and Time on Market ...... 112

Table X. Equality of Coefficients Across Periods………...…………..………………..114

Table XI. Equality of LOSS and GAIN Coefficients...……………………………..….115

Table XII. Loss Aversion and Listing Price- Demographics and Income Effect...…….115

Table XIII. Loss Aversion and Time on Market- Demographics and Income Effect….117

LIST OF FIGURES

Figure Page

Figure 1. Prospect Theory ...... 83

Figure 2. Volume of Transactions per quarter ...... 97

Figure 3. Reasons for Moving……………………………………………………...…....98

Figure 4. Transactions Per State ...... 99

CHAPTER I

INTRODUCTION

Housing market dynamics present several puzzles that cannot be explained completely by the neoclassical economic theory of rationality. For example, the strong serial correlations between housing prices and trading volume, the negative correlation between prices and time on the market and the swift changes in prices require a new approach of finding plausible explanations. Several new theories have emerged to address that need. The first is housing equity constraints as analyzed and proposed by Stein (1995),

Lamont and Stein (1999), Genesove and Mayer(1997) and several others. Generally, home purchases are leveraged by mortgage loans requiring a down payment. Any decline in home prices has a negative impact on home owners who are equity constrained affecting the overall demand leading to further declines in the house prices.

The other more controversial theory is presented by Genesove and Mayer (2001) and relates to prospect theory and loss aversion. Homeowners behave asymmetrically towards losses and gains where sellers are averse to prospective losses and will attenuate the loss by higher asking prices at the cost of longer time on the market. In a booming housing market, the turnover in the housing market is higher than in a bust and the sales prices are closer to the asking prices. In a bust markets, however, the time on market is

76 longer, volume decreases and the asking prices are higher than the market expectations forcing sellers to not enter the market or to withdraw their properties. Genesove and Mayer

(2001) determine that in the Boston condominium market in 1990s, sellers who face nominal losses set higher asking price and have lower probability of sale increasing the time on market than those facing nominal gains. Similar results have been found by

Engelhardt (2003), Anenberg (2011) and Leung and Tsang (2013). Bokhari and Geltner

(2011), confirm Genesove and Mayer’s results and find similar loss aversion behavior in the commercial real estate market.

I follow the same line of theory in this study to examine the effects of loss aversion in the overall U.S. housing market. The main goal of this research is to draw upon the

Genesove and Mayer model, expand, and test the loss aversion effects on the overall U.S. market. I further enhance their model by delineating the effects of gain and loss by adopting the methodology of Bokhari and Geltner (2011). However, I make some changes related to calculating the loss aversion effects by incorporating a most probable sale price variable as proxy for value at time of listing. Additionally, I substitute the estimated loan-to-value ratio with a ratio of the actual equity-to-sale price to establish how equity constraints affect the behavior of the sellers. The main contribution to the literature is confirming the results of Genesove and Mayer on a national level and by providing a more accurate estimation of perceived loss. Several studies10 have estimated the loss aversion effects following

Genesove and Mayer’s framework but to the best of my knowledge, I am not aware of a study that aims to validate the results on a national level while presenting a more accurate proxy for estimated selling price. Additionally, I posit that the utilization of a most probable

10 Anenberg (2011) and Leung and Tsang (2013)

77 sales price as a proxy for value at time of listing brings additional accuracy to the estimations since it is based on an evaluation of the property value as part of an appraisal or broker market evaluation. I also expand the models by incorporating salary and demographics data to examine their effects on loss aversion and time on market.

The study utilizes a private database of home sale transactions collected as part of corporate relocations. The database includes individual transaction information related to selling a home for the period of 2003-2014. Generally, employers provide assistance to their employees as part of a corporate relocation program and outsource the managing of the home sale program to relocation management companies (RMC). The responsibility of the RMC is to administer the program by assisting the employee with listing and selling the property. As part of the home sale process, a most probable sales price is established to determine the value of the property prior to deciding on a listing price. I use the most probable sales price as a proxy of property value at time of listing to calculate the prospective loss or gain. Additionally, I calculate the gain and loss as the difference between the original purchase price and the most probable sales price to examine the separate effects on listing price and time on market.

The results of the study confirm that loss aversion affects the behavior of the sellers in the residential housing market. The private relocation home sale transactional data shows that when sellers face prospective losses they tend to set higher listing prices. The loss aversion also affects the probability of a house to be sold. I utilize Cox proportional hazard model where the hazard rate is the probability of a house to sell to determine the effects of loss aversion. The results show that loss aversion decreases the hazard rate by 0.45 percent for the overall period. I further split our sample period in three sub periods: pre-crisis, crisis

78 and post crisis. I use the bursting of the housing bubble as reference for a commencement of the crisis period thus our sub-periods are as follows- 2003:Q1- 2005:Q4; 2006:Q1-

2009:Q4; 2010:Q1- 2014:Q4. The crisis period of 2006:Q1 -2009:Q4 contains the housing market bubble burst and the financial crisis. The results show that loss aversion is more acute in time of crisis- sellers facing a prospective loss tend to set up a listing price 10% higher during the crisis period compared to the pre-crisis period. Similarly, the hazard rate of sale decreases by 0.54 percent during the crisis period compared to 0.05 percent during the pre-crisis period.

The remaining of the paper proceeds as follows: Section Prospect Theory and Loss

Aversion discusses the concept of prospect theory and loss aversion and provides an overview of the relevant research studies while section Genesove and Mayer Loss Aversion

Model details the theoretical model and empirical results of both the Genesove and Mayer loss aversion model and Bokhari and Geltner commercial real estate loss aversion model.

The Data & Sampling Process section details our data and sampling process and the section

Overview of Relocation Homesale Process section outlines the homesale process and the determination of most probable sales price. The Model and Hypothesis section discusses in detail our theoretical model and methodology and formerly presents our hypothesis. In the section Empirical Models and Variables I discuss the variable construction and the design of the models. The Empirical Results section presents and discusses our empirical findings and the Conclusion section concludes our study.

CHAPTER II

PROSPECT THEORY AND LOSS AVERSION OVERVIEW

Traditionally, the behavior of the market participants is modeled based on rational decision-making that incorporates all available information (De Bondt et al, 2008). Thus, neoclassical economic theory posits that rational sellers and rational buyers in the housing market will look at the current market price in order to determine the value of a property.

Studies, however, show that physiological biases may affect the decision making process of both sellers’ and buyers’.

Tversky and Kahneman (1974) and Kahneman and Tversky (1979) propose a direct challenge to the rational market assumptions when they introduce the concept of prospect theory. Alternative to expected utility, prospect theory incorporates the psychological aspects in choice behavior. Individuals determine their choice by weighing the losses and gains relative to a reference point. Additionally, the decreasing marginal sensitivity states that the individual decision maker will value additional unit of gain or loss less than the previous gain or loss. Last, decision makers exhibit loss aversion i.e. they are more sensitive to losses than gains. Figure shows the value function according to the prospect theory. The graph illustrates that the value function is steeper for losses than for equivalent gains resulting in loss aversion – the dislike of a loss is stronger than a preference towards

80 a gain. Further studies by Tversky and Kahneman find that on average a loss has twice as much psychological impact as an equivalent gain (Tversky and Kahneman, 1992).

In the world of financial markets, loss aversion translates into inconsistent behavior towards risk. Individuals may assume risk to protect sure losses while avoiding risk to protect existing wealth (De Bondt et al., 2008). According to the prospect theory, decreasing volume in a declining housing market is due to the unwillingness of sellers to accept prospective losses relative to their original purchase price. Sellers will delay a sale when they are faced with a loss. In a rational market framework, sellers should treat the original purchase price, the initial outlay, as a sunk cost.

The two studies that are most relevant to this paper are Genesove and Mayer (2001) and Bokhari and Geltner (2011) research so I will discuss in detail their theoretical models and empirical results in the next section and just briefly mention in this section their contribution to this line of research.

In the housing market framework, Genesove and Mayer (2001) publish the influential paper on loss aversion in the Boston area in the 1990s. The study finds that homeowners are loss averse relative to the original price they paid for the property; as a result they tend to list the property for above market price which leads to longer time on the market. Several studies have shown that loss aversion in sellers and reference price effects impact the time on market (Anglin et al., 2003).

Elliot Anenberg (2011) builds upon the Genesove and Mayer model (henceforth

GM model) by examining the effects of loss aversion and equity constrains on selling prices in the San Francisco Bay Area real estate market over an 18-year period. He finds larger effects of loss aversion and equity constraints on actual transactional prices. The

81 author concludes that the results of this study support previous research suggesting that during “market downturns, sellers become locked-in to their homes because of loss aversion and equity constraints” which slows down the market (p.75). Sellers are waiting for higher prices so homes stay longer in the market, which slows down the sales volume.

In the same line of research, Engelhardt (2003) examines the effects of loss aversion and equity constraints on mobility. He analyzes data from the National Longitudinal

Survey of Youth (NLSY79) on household moves across U.S. and determines that loss aversion significantly restricts mobility but he could not find evidence that low equity has similar effects. Engelhardt concludes that loss aversion is “an important housing market phenomenon across a broad spectrum of metropolitan areas” (p.172). The implication to the housing market is that preferences such as loss aversion rather than rational decision making have fundamental effects on the housing market dynamics and require careful consideration.

Similarly to Genesove and Mayer, Bokhari and Geltner (2011) examine the loss aversion and anchoring effects in the commercial real estate market. They find consistent results in the commercial real estate market. I examine in more detail their model and results in the next section. Leung and Tsang (2013) combine the two studies of Genesove and Mayer and Bokhari and Geltner and examine the loss aversion and anchoring effects from a theoretical point of view using market date from Hong Kong for the period 1992-

2006. The real estate market in Hong Kong exhibits large swings in prices and very high transactional volume. Previous studies focus primarily on macroeconomic factors such as

GDP, interest rates, land supply and other long run price determinants. The authors take a different route when examining the short-term dynamics and by focusing on behavioral

82 aspects of the market participants. They find strong evidence that both biases exist in determining housing prices.

Figure 1. Prospect Theory

CHAPTER III

GENESOVE AND MAYER LOSS AVERSION MODEL

In 2001, Genesove and Mayer publish the seminal paper on loss aversion in the housing market. The Genesove and Mayer model (GM model) examines the sellers’ behavior in the Boston area market in the 1990s within the prospect theory framework.

They examine the relationship between expected selling price, time on the market and the seller’s reservation price in the Boston condominium market during the period of 1990 and

1997. During this time, the Boston market exhibits an interesting behavior. During a bust, the inventories of homes for sale are quite large but prices are not adjusted leaving overpriced listings on the market unable to sell. This behavior suggests that sellers are not willing to “accept market prices for property in the down part of a cycle” (p.1235).

Genesove and Mayer posit that loss aversion could explain sellers’ choice of listing price and willingness to accept market offers. In a downward market, the value of a home could fall below the original price paid by the seller, thus loss averse sellers will offset that loss by determining a reservation price higher than in situation of a gain. The resulting asking price will be higher than the market valuation leading to longer time on market and possible higher transaction price. The results of the study confirm that a loss aversion plays a significant role in the determination of a listing price. Sellers whose property price is below

84 the original purchase price will set a listing price between 25 and 35 percent higher than other sellers.

Several other studies have expanded the GM model for other areas in the housing market both residential and commercial. In the next section, I will examine in detail the

GM model from theoretical and empirical point of view. I will also review a similar model for the commercial estate market presented by Sheharyar Bohkari and David Geitner

(henceforth BG model)11.

The model in this study expands some of the aspects of the GM model and BG model. In regards to GM model, I use national private data of home sale transactions as part of a corporate relocation to examine the loss aversion behavior. Additionally, I am able to observe the value of the property at time of entering the market rather than estimating the value using hedonic regression. I further enhance the study by examining the equity a seller has in the property rather than the loan-to-value to determine equity constraints on the decision to set asking price and accept offers. The BG model focuses on loss aversion and anchoring effect in the commercial real estate market. They enhance the

GM model by introducing an estimate for gain to examine the difference between the behaviors of sellers in situations of gain vs. loss. I incorporate their approach in distinguishing between sellers’ behavior when facing prospective losses and gains.

3.1 Review of Genesove and Mayer Theoretical Model

To determine loss aversion within the prospect theory both the reference point and the reservation price are essential. Genesove and Mayer (henceforth GM) select the original

11 Sheharyar Bohkari and David Geitner (2011)

85 purchase price as the reference point. Reservation price, the price the seller sets to offset the prospective loss, cannot be observed. To overcome this challenge, GM use listing price, listing entry and the transaction (sale) price to infer changes in the reservation price. They develop an upper and lower bound model to determine loss aversion since the extent to which a seller may have over or under paid for the property originally cannot be measured.

The authors posit that regressing the “list price on the observed loss when controlling for the previous sale price, yields a lower bound for the true coefficient on loss, while not controlling for the previous sales prices provides the upper bound of the true effect”

(p.1238).

The authors start with specifying their ideal model where the listing price List is a linear function of an indicator for a LOSS and the expected selling price in the quarter of listing μit . All prices are in log form. The coefficient associated with the variable LOSS m measures the loss aversion factor.

List= α0 + α1μit + m LOSS* ist +εit (1)

To determine the expected selling price, they compute a price index from a hedonic regression on all property sales between 1982 and 1997 that could be matched with property specifics in the Assessor’s office to obtain the attributes for the hedonic regression. The authors regress the log of the selling price on the quarterly dummies for the period and several of the property attributes. The price index is then used to determine

LOSS as the difference between the original sales price and the price index as proxy for estimated selling price. The same price index is used to determine the loan-to-value (LTV) ratios. LOSS* is the higher of the differences between the log of the previous selling price, i.e. original purchase price, and the expected log selling price and zero. The true LOSS*

86 has two components – the change in the market price index between the quarter of the original purchase and the quarter of the listing and the overpayment/underpayment when seller purchased the property. Unfortunately, the true LOSS* cannot be observed thus the authors substitute with a noisy proxy- the difference between the original purchase price and the predicted price from the hedonic equation using the price index.

The authors perform two-stage estimation. First they obtain the β(parameter of the vector of observable property attributes) and δ (time effect that shifts the price proportionally) estimates from the following equation 2 by regressing the selling price on the quarter dummies and the attributes. Then they substitute the estimated parameters in equation 3 to obtain estimates for m

12 μit= = Xi β +δt + νi (2)

13 ηit= α1νi + m((δs –δt + wis)+ - (δs –δt +νi +wis)+) +εit (3)

The second stage adds the residual from the previous regression, ν + w, as proxy for the unobserved quality ν. The model is shown below.

14 List= α0 + α1 Xi β + α1δt + α1 (νi +wis)+ m LOSS* ist +uit (4)

The equation above addresses one of the implications of prospect theory- there is sensitivity around the reference point in regards to loss and gain, i.e. the value function is steeper for losses than gains. The GM model only models loss and not gain. As a result the coefficient m can be interpreted as the “differential effect of a loss relative to gain.” The reason the authors are focused on the loss is due to the higher sensitivity to the reference

12 X is the vector of observable attributes; νi is the unobservable quality 13 ηit is the error term from the regression of List price on LOSS and X ( vector of attributes) ;(δs –δt) is the change in market price index between the quarter of the original purchase and the quarter of listing; wis Is the overpayment/underpayment at time of the original purchase; νi is the unobserved quality 14 The equation is referred to as Model II in the original study

87 point related to losses than gains as shown in Figure . The next section outlines the empirical results and implications of this study.

3.2 Genesove and Mayer Empirical Results and Implications

The study addresses several questions from an empirical point of view. In addition to analyzing the effect of loss aversion on the asking price and days on market, the authors also address the notion that sellers calculate losses in nominal rather than real terms.

Additionally, they examine how experience may affect the behavior towards losses by analyzing the behavior of investors vs. occupant owners. For the purpose of this study, I will focus on the results related to the estimation of nominal loss aversion effects on asking price and on days on market since they have direct relationship to our model.

To examine the relationship between list price and the prospective losses, the authors develop six models. The model below is a representation of the main independent variables included in the OLS regression.

Log Original Purchase Price = α + mLOSS + β1 LTV + β2Estimated Value in 1990

+ β3Estimated Price Index + β4Months Since Last Sale + βQ Dummy variables for quarter of entry +ε

Model 1 includes the following variables- LOSS, LTV, Estimated value in 1990,

Estimated price index and the months since last sale. The main purpose of the estimation is to establish an upper bound of the true effects of loss aversion. In the second model, the authors include and additional explanatory variable- the difference between the original purchase price and the estimated value at the time of the quarter of the original sale as proxy for the unobserved quality. The purpose of the estimation of the model is to provide the lower bound of the true effect of loss aversion. In models 3 and 4, the authors add the

88 quadratic loss term to examine the behavior of sellers when faced with larger losses. In the last two models, they include dummies for the quarters of listing. The results from the estimations suggest that loss aversion has an effect on the listing price – the true effect lies in the range of 0.25 and 0.35. Since the asking price is a function of the LTV, it is important to determine the effect on the list price. The results show a positive and significant effect of LTV, i.e. sellers with high loan-to-value will set up higher asking prices.

In regards to time on market analysis, GM models the time on market in weeks as a hazard function. The hazard rate is the probability that a home will sell within a given week. They estimate the parameters by Cox‘s partial likelihood method. The results show that a 10 percent increase in loss equates to 3% decrease in probability of sale which translates in longer time on market.

In conclusion, the empirical results of the study confirm that sellers facing a prospective loss will set higher asking prices and attain higher selling price but have a lower hazard rate of sale.

3.3 Bokhari and Geltner Commercial Real Estate Model

Bokharti and Geltner (2011) use commercial real estate data from Real Capital

Analytics for the period of January 2001 to December 2009. The data consists of all commercial property sales in the United States with a price greater than $5,000,000. The authors construct their model to be able to estimate both the effects of loss aversion and anchoring15 similarly to GM.

15 Anchoring refers to the buyer using the asking price as an anchor to determine the value of a property affecting the buyer’s ability to adjust the value and arrive at rational market value.

They specify that the log asking price is a liner function of the expected log selling price in the quarter of listing. They define a variable reference called RF *(reference point) as the difference between the previous log selling price and the expected log selling price.

The true reference point term is defined below. (Qf – Qe) is the change in the market price index between the quarter of purchase and the quarter of listing and wif is the over- or underpayment of the seller at time of purchase. When RF*>0 , seller faces a prospective loss.

RF*=(μif + wif – μie)= (Qf – Qe) + wif (5)

They further break the RF* into loss and gain to examine the different effects.

Similar to GM, they examine the time on market by constructing a Cox proportional hazard model and setting the hazard rate to be the probability of sale.

The empirical results are very close to the GM study. The authors confirm the existence of loss aversion in the commercial real estate market. Additionally, the results confirm the asymmetrical behavior of sellers towards gains and losses. The coefficient for

LOSS was larger in magnitude than the one for GAIN- 0.38 to 0.22. In regards to time on market effects, the results are consistent with the GM study. Coefficient for LOSS is negative -0.32 suggesting that loss aversion behavior leads to reduction in the weekly sale hazard.

CHAPTER IV OVERVIEW OF THE RELOCATION HOMESALE PROCESS

Companies can offer different programs and tools to assist employees with the disposition of their home in the old location as part of the relocation16. Selling a home has a significant tax implication when relocation is concerned because of the additional support from the employers. The homesale process tax implications are governed by an IRS Ruling

72-339 and 2005-74 and warrant a separate discussion but the general rule is that the sale of a residence through two separate transactions in a relocation-assisted programs generally results in more favorable tax treatment than disposal through one sale. As such either the employer or the relocation management company (RMC) serve as a buyer in the first transaction where a bona fide offer is presented to the employee. Subsequently, the employer or the RMC acting as an agent of the employer sells the property to a third party buyer, i.e. outside buyer. It is very important that the homesale program is structured properly as two separate sales.

Before accepting the offer, the employee markets the home to determine whether a higher price can be obtained. If a third party buyer, i.e. outside buyer makes a bona fide

16 For more information on different homesale programs, please refer to the section “Overview of relocation homesale process” in Essay 1

91 purchase offer, the employer or RMC will raise its offer to equal the outside buyer's, hence the term ``amended value''.

To satisfy the two independent sale requirement of the IRS, the employee contracts to sell to the employer/RMC, which then enters into its own listing agreement with a real estate broker, attempting to contract with and sell the home to the same third party buyer identified by the employee. Thus, the relocating employee is unaffected by the outcome of the second transaction. In general, the employer will require a certain period of marketing time before appraisals are performed and an offer is extended if an outside buyer is not found. In the next section I explain in detail the home marketing process since listing price and most probable sales price are crucial to our study.

4.1. Home Marketing Process

To complete the discussion of the homesale process it is very important to outline the home marketing process which includes determining listing price, selecting a proper broker and agent and also discussing the determination of property value and arriving at a most probable sales price.

Since this study is utilizing private RMC homesale transactional data, I will focus on presenting the home marketing process as part of the services offered by the RMC.

Well-established marketing assistance process allows for more accurate determination of property value and helps the relocating employee, employers, and RMCs with the reduction of properties not being able to be sold and going into inventory17. The process starts with

17 The term “inventory” means that a bona fide third party buyer, i.e. outside buyer, has not been found prior to presenting the transferee with an offer so the employer will have to purchase the home from the employee and service and market the home until such third party buyer is found. The sale is structured in such a way as to show in good faith the two sale transactions as governed by the 11 elements of a complaint homesale program.

92 the proper selection of two experienced brokers through the preferred network of the RMC.

The brokers assign an agent based on the information received from the RMC. Usually, the relocating employee will meet with two agents before selecting the listing agent. The agents’ responsibility is to gather information necessary to complete the ERC Broker’s

Market Analysis and Strategy Report (BMA)18 . Producing a BMA will enable the agent to compile a comprehensive analysis to arrive at a projected sale price (most probable sale price) as a proxy for estimated market value and recommend a listing price. The results of the BMA are shared with the RMC and reviewed with the relocating employee. The RMC will also make a recommendation and subsequently the employee will select their listing agent. The selected agent is notified and listing is taken, incorporating an ``exclusion clause'' which will allow the employee to sell their property to the RMC as an agent of the employer. It is important to note, that many employers will have guidelines around determining the listing price based on the BMA values to encourage proper pricing at time of listing which increases probability of selling the property during the employee marketing time. The above-described pre-marketing process is designed to help achieve open market sales and the realization of maximum market value. It is important to note that this review process could minimize the effect of loss aversion since the RMC employees are not the

18 In 1989, the BMA form was introduced to be used in conjunction with the appraisal process during a traditional corporate homesale program. The primary reasons for the BMA process are to provide valuable information as part of the overall homesale assistance program and assist in devising home-marketing strategy. The BMA provides the most likely sales price ``as is'' and ``with repairs and improvements'' as well as the most likely net price ``as is'' and ``with repairs and improvements'' assuming a reasonable marketing time which is typically no more than 120 days. It is a three-page form supplying such information as likely financing, property details and local market recent sales. It enables the real estate broker to analyze and report on the subject property’s condition, other listed properties with which the subject must compete, any recently sold properties and most importantly the marketability of subject property.

93 one facing the loss thus they are much more objective in the determination of a proper listing price.

CHAPTER V

DATA AND SAMPLING PROCESS

This study uses private homesale transactions as part of corporate relocation moves.

Generally, the authorizations are sent to a Relocation Management Company (RMC) once an offer is extended to the employee and they are ready to start the process. The RMC will start the process of administering the program benefits. Depending on the homesale program offered as part of the policy associated with a particular move, the RMC will start either the listing process or the listing and appraisal processes simultaneously. The data is gathered as part of the home sale process and recorded in a database. Table I shows the variables in the database incorporated in this study that have been gathered as part of home sale transaction.

Table I Database Variables

Variable

Original Price paid for Home By Transferee Price RMC sold the property to Outside Buyer (Final Sales price)

Date property sold to Outside buyer

Most probable sale price- average of 2 BMAs( Broker Market Assessment of Property Market value- 3-6 months in the future)

Listing price

Date of Listing

Acquisition Price ( Price RMC pays to transferee to acquire the property), it is referred to sales price in our study

Proration Date ( Date RMC acquires property from transferee) Transferee equity in property at time of acquisition ( EQ= Acquisition price - Mortgage Balance – Prorated Interest + Escrow- Property Taxes – Homeowner association dues - Repairs & Concessions

I start by splitting the data set into two main categories- employees that have accepted the relocation and have decided to continue with the process and employees who have rejected the company offer. I drop any home sale transactions that have not been completed, i.e. the relocating employee has decided not to continue with the relocation and has withdrawn the home from the listing with the intention not to re-enter as part of the relocation program.

The second phase in the sampling process is to review each observation to ensure that no missing data. Any transaction with missing data that is crucial to the study has been dropped. Additionally, I review the transactions for accuracy in recording of the data by examining the listing price and original purchase price of each transaction for reasonableness.

The original sample size of all homesale transactions from 2001 to Q4 2014 is

63,690 who accepted the transfer and 2,686 who rejected the transfer. Because of very small sample size and data integrity issues, I drop the years 2001- 2002 which does not result in a significant loss of observations. The total sample size is 53,483 homesale transactions for the period 2003:Q1 to 2014:Q4. I further review each transaction for accuracy and for missing data. The final sample size consists of 36,316 home sale

96 transactions for the period 2003:Q1 to 2014:Q4. Figure 2 shows the transaction volume per quarter. The transaction volume increases in the quarters prior to the bursting of the housing bubble and shows a dramatic decrease during the financial crisis. It is important to note that relocations are also cyclical in nature. Most relocation moves are completed during the summer months as shown by the fluctuations in volume per quarter- Q2 and Q3 of each year exhibit larger volume of transactions compared to Q1 and Q2.

Figure 2. Volume of Transactions per quarter

The 2012- 2013 Current Population Survey (CPS) shows that between 2012 and

97 important to note that the relocating employees participate in the same housing market as all of the moves regardless of what the reason is, thus there are exposed to the same market forces as the rest of the migrating population.

Figure 3. Reasons for Moving

Figure 4 shows the distribution of transactions per state. There is a good representation of many states and several of the states such as Texas, Michigan and Illinois had significant declines in housing prices as part of the bubble burst.

Figure 4. Transactions Per State

The next section will provide an overview of the home sale process as part of a corporate relocation and describe in detail the determination of most probable sales price.

CHAPTER VI

MODEL AND HYPOTHESES

6.1 Theoretical Model

I draw upon the GM and BG models but I introduce several changes to the models.

First, I substitute the noisy proxy of estimated selling price derived by hedonic equation in both studies, with a “most probable sales price” determined by the BMA process as described in the previous section. Second, instead of a noisy approximation of LTV, I use the actual equity of the seller rather than an estimated mortgage balance. I substitute the loan-to-value ratio with equity- to-sale ratio19. GM use 80 percent threshold for the LTV cut off, I inverse that and translate into equity terms. Thus, I impose a cut off of 20 percent equity-to-sale price ratio. Third, I incorporate the concept of GAIN as defined by BG in their model, which allows us to estimate an unbiased coefficient for GAIN rather than present a range as in the GM model.

Thus, I define our theoretical model as follows:

List= α0 + α1MPSPil + m LOSS ipl +εil (6)

19 The sales price is the so-called acquisition price, this is the price offered to the relocation employee, as part of the two-transactions home sale. The acquisition price could be the same or different than the sales price to the third party bona fide buyer

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Here i is defined as the property, p corresponds to the month of the original purchase, and l corresponds to the month of listing. MPSP is defined as the most probable sales price determined in the month of listing. LOSS is the reference point defined as the difference between the log of the original purchase price (Pip) and the log of the most probable sale price as proxy for value at time of entering the market, i.e. the month of listing ( equation 6). If there are no behavioral effects m will equal 0.

LOSS ipl = (Pip – MPSPil) (7)

In the final model, similarly to BG, I break out the LOSS into two components to capture the effects of both loss and gain. I expect to see estimate m to be larger than g and

I will examine their difference in magnitude.

List= α0 + α1MPSPil + m LOSS ipl + g GAIN ipl + εil (8)

6.2 Hypotheses I examine the effects of loss aversion on the housing market from two points of view. On one hand, I expect that sellers will set a higher listing price to attenuate for the loss they are facing relative to a reference point (the original purchase price). On the other hand, setting up a listing price that is higher than the market value of the property affects the ability of the property to sell increasing its time on the market. Now, I formally state the hypotheses:

H1: A loss averse seller offsets a prospective loss by setting a higher listing price.

H2: The higher prospective loss and set listing price, the higher the costs of a longer time on market is.

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6.3 Methodology

To test the hypotheses I will examine each one of the effects separately. To estimate the loss aversion effect on listing price, I will perform a multiple regression analysis of the log of listing price on the calculated LOSS and report statistics robust to heteroscedasticity.

To examine how loss aversion affects the time on the market, I will define the time on market in weeks as a dependent variable and estimate the parameters of LOSS and GAIN by Cox’s partial likelihood method20. The Cox proportional hazard model assumes that covariates are multiplicatively related to a hazard rate so the effect parameters can be estimated without any consideration of the hazard function reported as hazard ratio. I follow the methodology as described by BG and GM where the hazard rate is the probability that a property sells within a given week. Following prospect theory when a seller faces a prospective loss, they will set a higher reservation price thus facing a longer time on market, i.e. a lower hazard rate of sale.

To further examine the effect of the financial crisis on the behavior of the sellers, I segment our sample in three sub-samples to cover the pre-crisis, crisis and post crisis periods. Our sub-sample periods are as follows – 2003:Q1- 2005:Q4; 2006:Q1-2009:Q4 and 2010:Q1-2014:Q4 to cover each of the three periods. I will perform our estimation on the full sample and on each of the sub-samples to determine if the financial crisis has changed the behavior of the sellers. I expect that the loss aversion has become more acute after the recent crisis and I expect difference in the magnitude of the coefficients.

20 Kumar and Klefsjö (1994) provide an overview of the Cox proportional hazard models, while Larsen and Park (1989) provide an overview of how to employ the model with real estate data

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CHAPTER VII

EMPIRICAL MODELS AND VARIABLES

In this section, I develop the model that reflects the prospect theory and the loss aversion heuristic. The model developed is similar to the GM model and BG model but I incorporate the changes described in the previous section, which eliminate the need to estimate the home value by hedonic regression. Furthermore, I split the sample into the three sub-periods as established in the Methodology section of pre-crisis, crisis and post crisis. To examine the marginal effect of gain and loss when the gain and loss diminish I include the quadratic gain and loss in our models.

7.1 Empirical Models

Here I formalize the main empirical models and discuss the construction of the variables included in the model. I construct three sets of models. The first two models

(Equations 9 and 10) empirically test the loss aversion effects on the listing price, while the third model (Equation 11) follows the GM specification for testing the effect on time on market.

Model I

List= α0 + m1 LOSS ipl + g1 GAIN ipl + ETV + Months since last sale + εil (9)

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Model II

2 2 List= α0 + m1 LOSS ipl + m2LOSS ipl + g2 GAIN ipl + ETV + Months since last sale + εil (10)

Model III

TOM= α0 + m1 LOSS ipl + g1 GAIN ipl + Months since last sale + εil (11)

7.2 Variables Construction

I further discuss the construction of the variables in the model. Table II shows the variables and a brief explanation of their construction and expected signs. The variables included in the models follow the GM and BG study. The main difference is in the construction of the LOSS, GAIN and ETV variables, so I will expand further how they have been calculated.

LOSS and GAIN variables capture the loss aversion effects on the listing price and time on market. I first calculate the difference between the log of the original purchase price and the log of the most probable sales price as proxy for value at time of listing. Then,

I construct the variable for LOSS by truncating the difference from below at zero; subsequently, I create the GAIN variable by truncating the difference above at zero. I expect that the coefficients for loss and gain to be positive but different in magnitude with the coefficient of LOSS to be higher than GAIN to capture the asymmetry of behavior from prospect theory perspective.

The ETV variable is constructed differently than the GM model. In the GM model, the authors calculate the LTV value by determining the estimated value of the property by constructing a price index of all sold properties for the period of 1982 and 1997 that could

104 be matched to assessors’ data on property characteristics. In our study, I construct the ETV by using the actual equity of the seller to their final selling price (acquisition price).

Generally in a relocation home sale transactions, the customary selling costs and broker’s commission are covered by the employer, thus the relocating employee will receive their equity minus any repairs or selling concessions as a payout from the transaction. By using the actual equity to sale ratio, I construct a much less noisier variable to explain the effect of equity constraints on listing price and time on market.

Months since last sale are constructed in a similar fashion to the GM study- the difference between the month the property is sold and the month of the original purchase.

The variable not only serves as a control related to time the property has been held in possession but it also possesses some behavioral aspects. The longer a property is held by an owner, the more attached the owner is and thus the higher the perceived value of the property due to the intangible value of experiences and memories associated with owning the home. The psychological impact of selling a home vs an investment in a house is much bigger from reference point of view increasing the perceived loss and putting a large value of worth.

Time on market (TOM) is determined as the difference between the dates of the listing and contract with the actual bona fide third party buyer in the relocation two-sale home sale transaction. Since our database is constructed of sold properties, I do not need to censor any of the transactions as GM and BG do to determine time on market.

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Table II Variable Legend

Variables Type Expected Sign Explanation

LIST Dependent Variable Log of listing price

LOSS Continuous + Difference between original purchase price and the most probable sales price truncated below at 0

GAIN Continuous + Difference between original purchase price and the most probable sales price truncated above at 0

ETV Continuous + The lesser of the difference between the equity to sales price ratio (ETV) and .20 or 0. ETV is calculated as a ratio of equity to sales (acquisition) price.

Months since last Continuous _ The difference between the original sale purchase month and the month of sale

TOM Dependent Variable The time on the market calculated as the time between the listing date and the date of sale in weeks

Income Continuous _ The log of the annual salary of the homeowner

Gender Categorical ? The variable is set at 1 if the homeowner has identified himself as male and 0 for female

Family Categorical + The variable is set at 1 if the homeowner is married or have a partner and 0 for single

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CHAPTER VIII

EMPIRICAL RESULTS

This section reviews the empirical analysis and the testing of the hypotheses. I present the estimation results for loss aversion effects on listing price and the time on market separately. I also examine the models for multicollinearity. All standard errors from the OLS regression models are robust for heteroscedasticity.

8.1 Descriptive Statistics

Table III reports summary statistics of the key variables for the full sample while

Table IV, Table V and Table VI present the descriptive statistics for each of the sub- periods. By reviewing the Variance Inflation factors (VIF), I can determine that multicollinearity is not an issue in the models. All VIFs are less than 10. When comparing the means between the sub-periods, I can see that LOSS increases during the crisis period in comparison to the pre-crisis period while GAIN has a significant change in the post- period. I can interpret the change in GAIN (the difference between the log of purchase price and log of selling price) as a result of the recent crisis diminishing the housing wealth and equity of the homeowners. The time on the market (TOM) increases during the crisis and it shows higher fluctuations as seen in the higher standard deviation during the crisis.

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Table III Descriptive statistics – 2003:Q1- 2014:Q4

Variable Label N Mean Std Dev Min Max VIF LIST 36316 5.47 0.24 4.43 6.90 ETV 36316 0.13 0.20 0 0.82 1.20 GAIN 36316 -0.10 0.13 -1.06 0 1.24 LOSS 36316 0.03 0.06 0 0.52 1.17 Months Since Last Sale 36316 66.47 49.71 6.0 408.0 1.19 TOM (weeks) 36316 11.12 11.43 0 168.57

Table IV Descriptive statistics – Pre- Crisis Period 2003:Q1- 2005:Q4

Variable Label N Mean Std Dev Min Max VIF LIST 7422 5.43 0.24 4.43 6.51 ETV 7422 0.15 0.19 0 0.81 1.24 GAIN 7422 -0.13 0.14 -1.03 0 1.29 LOSS 7422 0.03 0.07 0 0.52 1.13 Months Since Last Sale 7422 58.99 47.55 6.0 324 1.21 TOM (weeks) 7422 9.70 9.99 0 168.57

Table V Descriptive statistics –Crisis Period 2006:Q1- 2009:Q4

Variable Label N Mean Std Dev Min Max VIF LIST 14703 5.46 0.23 4.44 6.61 ETV 14703 0.13 0.19 0 0.81 1.22 GAIN 14703 -0.13 0.14 -1.01 0 1.25 LOSS 14703 0.03 0.06 0 0.45 1.15 Months Since Last Sale 14703 61.24 48.87 6.0 360 1.24 TOM (weeks) 12.72 12.40 0 161.71 14703

Table VI Descriptive statistics – Post Crisis Period 2010:Q1- 2014:Q4

Variable Label N Mean Std Dev Min Max VIF LIST 14191 5.49 0.24 4.56 6.90 ETV 14191 0.12 0.20 0 0.82 1.66 GAIN 14191 -0.07 0.11 -1.06 0 1.22 LOSS 14191 0.03 0.07 0 1.03 1.15 Months Since Last Sale 14191 75.82 51.23 0 408.0 1.21 TOM (weeks) 14191 10.21 10.89 0 153.71

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8.2 Empirical Results

In this section, I present the main results related to our hypotheses in two sections.

Table VII presents the main results of our OLS regression while Table VIII presents our estimation results related to the marginal effect where I include the quadratic loss and quadratic gain.

Table IX outlines the results from the proportional hazard model testing the effects of loss aversion on time on market. Overall, our results confirm the results of both GM and

BG that loss aversion has an effect both on the listing price and time on market.

Additionally, I introduce a new dimension to the models by including demographic and income data. Results are presented in Tables XII and XIII.

8.2.1 Loss Aversion Estimation Results

The first column of Table VIII outlines results related to the full sample while the other three columns present the results from the sub periods. The regression of log listing price on prospective gains and losses and the equity ratio confirm that losses have a greater effect than gains: the coefficient of LOSS is higher than the GAIN coefficient. The coefficient of 0.5689 for LOSS which is the point elasticity based on log differences suggests that a 10 percent increase on a prospective loss will lead to a listing price 5.689 percent higher than otherwise a seller would have set. The results are higher in magnitude when compared to GM and BG models. GM reports a range of 0.25 to 0.35 and BG reports

0.38 as LOSS coefficients. The positive coefficient of GAIN suggests that sellers facing a gain will set a price lower than they would otherwise do. GM does not report coefficient

109 for GAIN and the comparable finding in the commercial real estate study of BG is 0.223, much smaller than what our results show. The positive and statistically significant coefficient of ETV suggests that equity constrained households will compensate by increasing the listing price.

The results from Models IA-IC show that in times of market decline, loss aversion behavior is much more acute- the difference in magnitude of the LOSS coefficient between the pre- crisis and crisis. I also see that the effect of loss aversion may have lingering effects because the LOSS coefficient post crisis is still higher in magnitude than the pre-crisis.

One of the reasons is that the recent housing bust and financial crisis lead to substantial losses to housing wealth thus more sellers are facing prospective losses than prior to the housing bubble burst.

The marginal effect of LOSS is significant in the pre-crisis, post crisis and the overall period; but for changes during the crisis, there is no evidence of the diminishing effects of the loss. I would expect that sellers cannot raise the price too high because they will outprice themselves so the marginal increases in the list price will decrease with the size of the loss. One possible explanation is that when facing a large loss, the seller does not enter the market at all or withdraws. The sample data does not allow for examining the behavior of sellers with realized sales vs. ones who withdraw. The quadratic coefficient for gain is positive which suggests that marginal effect of gains diminishes as the gains increase.

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Table VII Loss Aversion and Listing Price

Variable Model I Model IA Model IB Model IC Dependent Variable Full Sample Pre- Crisis Crisis Post Crisis Log LIST Price (2003:Q1-2005:Q4) (2006:Q1-2009:Q4) (2010:Q1-2014:Q4) Intercept 5.4288***(2895.70) 5.3874***(1092.14) 5.4399***(1639.46) 5.4703***(1932.53) LOSS 0.5689 *** ( 29.58) 0.4479***(14.69) 0.4986***(18.26) 0.5495***(18.02) GAIN 0.3485***(35.64) 0.4389***(20.49) 0.4777***(35.62) 0.3728***(19.92) ETV 0.1880***(30.75) 0.3973***(23.58) 0.2588***(22.58) 0.1503***(15.88) Months since last -0.0008***(- -0.0016***(-19.10) -0.0009***(-23.50) -0.0005***(-5.12) sale 10.32) R2 0.1235 0.2479 0.1766 0.1077 Number 36316 7422 14703 14191 Observations Note: t-statistics robust for heteroscedasticity are shown in parentheses. Statistical significance at 10%, 5%, and 1% levels is indicated by * , **, and *** , respectively.

Table VIII Marginal Effect of Loss Aversion on Listing Price

The results of the OLS regression robust for heteroscedasticity are presented below. The dependent variable is Log of Listing Price. Model IA, Model IB and Model IC are estimated for the sub-periods of pre-crisis, crisis and post crisis. The quadratic term of LOSS captures the falling marginal response to prospective loss. The quadratic term of GAIN in addition to the quadratic term of LOSS captures the diminishing marginal effect of gain as gain increases.

Variable Model II Model IIA Model IIB Model IIC Dependent Variable Full Sample Pre- Crisis Crisis Post Crisis Log LIST Price (2003:Q1-2005:Q4) (2006:Q1-2009:Q4) (2010:Q1-2014:Q4) Intercept 5.4453***(3154.94) 5.4191***(1085.04) 5.4626***(1720.66) 5.4703***(1932.53) LOSS 0.7454 *** (20.12) 0.5938***(9.72) 0.6334***(6.77) 0.7638***(15.41) LOSS_SQR -0.3216**(-2.45) -0.3670**(3.81) Not significant -0.4621 **(-3.10) GAIN_SQR 0.7593***(34.46) 0.9078***(9.76) 0.9587***(24.63) 0.7718***(17.85) ETV 0.1995***(32.89) 0.4114***(23.58) 0.2720***(23.41) 0.1835***(14.39) Months since last sale -0.0008***(-9.95) -0.0011***(-18.10) -0.0009***(-21.13) -0.0004***(-4.84) R2 0.12240 0.2365 0.1680 0.1077 Number 36316 7422 14703 14191 Observations Note: t-statistics robust for heteroscedasticity are shown in parentheses. Statistical significance at 10%, 5%, and 1% levels is indicated by * , **, and *** , respectively.

8.2.2 Time on Market Estimation Results

I expect that sellers who set reservation price higher may also face longer time on the market. The results from the proportional hazard model are presented in Table IX. The model examines the effects of loss aversion on the probability to sell in a given week ( our

111 hazard rate). The rate is specified as as h(t) = h0(t)exp(αX) where X is the vector of covariants and α is the vector of coefficients. The LOSS terms are negative and highly significant suggesting that longer time on the market decreases the probability of sale. For example, the coefficients from Model III can be interpreted as follows: for a 10 percent loss, the reduction in the probability to sell is 0.45 percent while for 10 percent gain, the increase probability to sell is 1.63 percent. Similarly, to the effects of loss aversion on listing price, I see that during the crisis the reduction of probability is much larger when compared to the pre-crisis period- 0.05 percent in pre-crisis vs. 0.54 percent during the crisis and 0.73 percent post crisis confirming our expectations that sellers increase listing price to attenuate for prospective loss at the cost of longer time on the market. Both GM and BG report larger percentage impact of 3 percent. One of the reasons why I find different smaller percentage impact is due to the nature of our sample. As part of the relocation program, many employers offer buyouts, i.e. purchase the property from the home owner after a certain period of time, and are able to absorb the losses to dispose of the property to avoid paying carrying cost for maintaining.

Overall, the results confirm the findings of BG and GM that nominal loss aversion has significant effect on the dynamics of the housing market by impacting the listing and sales prices and time on market not only on regional or city level but also on a national level.

Table IX Loss Aversion and Time on Market

The results of the Cox proportional hazard (Breslow) model are presented below. The dependent variable is Time on Market in weeks. The hazard rate is the probability of the property to be sold in a given week specified as h(t) = h0(t)exp(αX) where X is the vector of covariants and α is the vector of coefficients. Model IA, Model IB and Model IC are estimated for the sub-periods of pre-crisis, crisis and post crisis.

Variable Model III Model IIIA Model IIIB Model IIIC Dependent Variable Full Sample Pre- Crisis Crisis Post Crisis DOM in weeks (2003:Q1-2005:Q4) (2006:Q1-2009:Q4) (2010:Q1-2014:Q4) GAIN 0.1673**(0.004) 0.3291***(0.102) 0.2948***(0.072) 0.1560**(0.086)

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LOSS -0.0453***(0.003) -0.0051***(0.005) -0.0541*** -0.0731***(0.141) Months since last sale -0.0007***(0.000) -0.0007**(0.000) -0.0009***(0.000) -0.0008***(0.000) Number Observations 36316 7422 14703 14191 Note: Standard errors are shown in parentheses. Statistical significance at 10%, 5%, and 1% levels is indicated by * , **, and *** , respectively.

8.2.3 Statistical Significance of the Coefficients

In order to determine if the estimated parameters are significantly different between all of the periods, I perform a Chow test. There are two ways a Chow test can be performed to test the parameters of different regressions- one is creating interaction terms and the other is by performing a pooled regression and using the following formula to calculate

Chow statistics

Chow Statistics = {[SSEpooled –(SSE_1 +SSE_2)]/K}/[(SSE_1-

SSE_2)/(N_1+N_2-2*K)]

SSE_1 and SSE_2 are the error sum of squares from the separate regression for each of the periods, SSE pooled is the error sum of squares from the pooled(constrained) regression. K is the number of estimated parameters in the model, and N_1 and N_2 are the observations of the two period samples. The resulting test statistics is distributed as F

(K, (N_1+N_2-2*K))

I performed the pooled regression for 2003-2009 to compare the parameters for the two periods prior to the crisis and during the crisis. Similarly, I performed a pooled regression on the period 2006-2014 to compare crisis period against the after the crisis. The

Chow statistics for the first test is 22.39 with a critical value of F=3.02 which rejects the null hypothesis that the parameters of the two regressions are not statistically different. I perform the exact same test and calculations for the crisis and after crisis periods. The

Chow statistics is 255.51 which is higher than the critical value, thus rejecting the null

113 hypothesis. The results of the Chow test allow us to conclude that the parameters are significantly different from each other in each of the periods when testing the full model.

Additionally, I perform a test to determine if each of the individual variables LOSS and GAIN are also significant across the three periods. I performed pooled regression for

2002-2009 and 2006-2014 periods on the model dropping the GAIN variable to determine if the LOSS variable is significantly different across all the periods. I perform a similar test on the model by dropping the LOSS variable. Both results are displayed in Panel B and

Panel C below and show that both parameters of LOSS and GAIN are significantly different across periods.

Table X Equality of Coefficients Across Periods

The results of the Chow test for equality of coefficients across the three periods are presented below. Panel A shows results related to the full model containing Loss and Gain variables while Panel B and Panel C show results related to models with only Loss or only Gain variable

Panel A Full Model Model VB Model VC Pre Crisis/ Crisis Crisis/Post Crisis (2003: Q1-2009:Q4) (2010:Q1-2014:Q4) Chow Test F= 22.39 F=255.51

Panel B LOSS Only Model Model VB Model VC Pre Crisis/ Crisis Crisis/Post (2003: Q1-2009:Q4) Crisis (2010:Q1- 2014:Q4) Chow Test F= 73.20 F=65.20

Panel C GAIN only model Model VB Model VC Pre Crisis/ Crisis Crisis/Post (2003: Q1-2009:Q4) Crisis (2010:Q1- 2014:Q4) Chow Test F= 107.29 F=346.10

I also examine if the parameters of the main variables LOSS and GAIN are statistically different in each of the models. The results are presented in Table XI. The overall model results reject the hypothesis that the parameters for LOSS and GAIN are equal at 5% level. Pre- crisis and during crisis periods show marginally statistically

114 different parameters while the test results after the crisis show a highly significant difference in the two estimated parameter.

Table XI Equality of LOSS and GAIN Coefficients

The results of the t- test for equality of coefficients within the same models are presented below. The covariance of the parameters is - 0.000072.

Model Model Model Model Full Sample Pre- Crisis Crisis Post Crisis Difference Between LOSS & GAIN coefficients (2003:Q1- (2006:Q1- (2010:Q1- 2005:Q4) 2009:Q4) 2014:Q4) Z-Score 2.20** 1.654* 1.76* 4.93*** Number Observations 36316 7422 14703 14191 Note:. Statistical significance at 10%, 5%, and 1% levels is indicated by * , **, and *** , respectively.

8.2.4. Demographics Effects on Loss Aversion and Time on Market To test if demographics have an effect on loss aversion I include gender and marital status categorical variables in the model. I set Gender to 1 if the seller is male and 0 if female. I set Family to be 1 if seller has indicated that they are married or have a partner and 0 otherwise. To explore further the relationship between loss and demographics, I introduce interaction terms. The interaction terms between Loss and Gender and Loss and

Family provide additional information of the effect of demographics on Loss. I also include the log of the annual salary of the homeowner as proxy for income.

Table XII Loss Aversion and Listing Price – Demographics and Income Effect

The results of the OLS regression robust for heteroscedasticity are presented below. The dependent variable is Log of Listing Price. Model IVA, Model IVB and Model IVC are estimated for the sub-periods of pre-crisis, crisis and post crisis.

Variable Model IV Model IVA Model IVB Model IVC Dependent Variable Full Sample Pre- Crisis Crisis Post Crisis Log LIST Price (2003:Q1-2005:Q4) (2006:Q1-2009:Q4) (2010:Q1-2014:Q4) Intercept 3.6328***(131.43) 3.8993***(57.54) 3.7326***(100.38) 3.0521***(58.06) LOSS 0.4425 *** (6.66) 0.1698* (1.85) 0.5440***(6.86) 0.4535***(3.31) GAIN 0.2569***(19.06) 0.3428***(5.44) 0.3621***(20.52) 0.1795***(7.27) ETV 0.2160***(27.25) 0.3352***(12.35) 0.1997***(15.49) 0.1440***(10.68) Months since last sale -0.0006***(-18.17) -0.0009***(-8.00) -0.0008***(-15.91) -0.0006***(-14.15) Gender 0.0261***(6.15) 0.0226* (1.67) 0.0118** (2.04) 0.0331***(5.02) Family 0.0953***(19.88) 0.0985***(6.45) 0.0851***(13.37) 0.0809***(9.17) Loss* Gender 0.0024 (0.04) 0.1017 (0.08) 0.0347 (0.44) 0.0249 (0.30) Loss*Family - 0.2043**(-3.24) -0.2327**(-2.34) -0.1190* (-1.64) -0.2224** (-2.29) Income 0.3381***(65.04) 0.2808***(20.52) 0.3101***(44.10) 0.4692***(45.53) Loss*Income 0.0018***(11.37) 0.0010**(1.98) 0.0020***(9.32) 0.0008**(3.59) R2 0.3120 0.3683 0.3488 0.3490 Number Observations 19257 2152 9307 7798

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Note: t-statistics robust for heteroscedasticity are shown in parentheses. Statistical significance at 10%, 5%, and 1% levels is indicated by * , **, and *** , respectively.

The inclusion of income and demographic factors definitely contributes to the model - the R2 increases compared to the main model’s results. Income is significant in all models meaning that higher income transferees are more loss averse. Additionally, I see that income’s parameter increases in magnitude during the crisis and continues to increase post crisis. One explanation could be that higher income homeowners generally have high valued homes that face larger losses during the crisis that may need to be recuperated once the home owner decides to dispose of the home.

The regression analysis shows that both gender and family have an effect on listing price. Being married leads to an increase in listing prices while being male leads to a decrease in listing price. I see that coefficients on Loss change in magnitude but continue to show the increased Loss aversion after the crisis as in the main model.

The regression results show that gender does not have an additional effect on Loss.

However, I see the opposite with marital status prior to the crisis and post crisis- being married has an effect on Loss. One explanation is that the effect and severity of the crisis have eliminated the difference related to demographics since the crisis was so wide spread.

In addition, I see that married homeowners may have a more subdued reaction to Loss aversion due to the additional pressures to sell the home and move to a new location so they may be more realistic in their approach.

I further explore the effect that gender family status and income may have on the time on market. Overall, I see that Gender and Family do not have a significant effect on time on market. However, the interaction term with loss has some effect on time on market.

I see that being married and exhibiting loss Aversion actually decreases the time on market.

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One explanation can be that homeowners with families have other determinants related to disposing of a property and have some time constraints related to school year and other factors related to the family. I can posit that the ability to carry two residencies may diminish due to the additional financial burdens of carrying a family thus affecting the way married homeowners exhibit loss Aversion. They may be more realistic in setting up listing price as I see in the previous regression results leading to higher probability to sell the home in a given week and thus decreases the time on market. The interaction term between

Loss and Gender continues to be insignificant. However, I see a change in the significance of the Loss and Family interaction terms during the crisis and after the crisis. Similarly, to the Loss aversion model, I observe that homeowners with higher income increase the time on market especially in the post crisis period. During the crisis, I see an adjustment where the effect on the probability to sell is less compared to the two other periods. One explanation is that during the crisis, higher priced home may have demanded a larger discount.

Table XIII Loss Aversion and Time on Market- Demographics and Income Effect

The results of the Cox proportional hazard (Breslow) model are presented below. The dependent variable is Time on Market in weeks. The hazard rate is the probability of the property to be sold in a given week specified as h(t) = h0(t)exp(αX) where X is the vector of covariants and α is the vector of coefficients. Model VA, Model VB and Model VC are estimated for the sub-periods of pre-crisis, crisis and post crisis.

Variable Model V Model VA Model VB Model VC Dependent Variable Full Sample Pre- Crisis Crisis Post Crisis DOM in weeks (2003:Q1-2005:Q4) (2006:Q1-2009:Q4) (2010:Q1- 2014:Q4) GAIN 0.0594* (0.062) -1.3555***(0.2139) 0.1025* (0.087) -0.3894** (0.132) LOSS -0.1859***(0.057) -0.1947***(0.061) -0.1670**(0.084) -0.42161*** (0.097) Months since last sale -0.0012***(0.000) -0.0022***(0.000) -0.0015***(0.000) -0.0016*** (0.000) Gender -0.0161 (0.020) 0.0024 (0.066) 0.0161 (0.029) -0.0234 (0.032) Family -0.0027 (0.024) -0.0092 (0.074) -0.0151 (0.032) -0.0245 (0.041) Loss * Gender 0.8666** (0.278) 0.0.6273 (0.641) 1.6799** (0.452) -0.0961 (0.425) Loss * Family 1.1815*** (0.277) 0.9253* (0.559) 1.0075***(0.454) 1.2576** (0.417) Income -0.2939***(0.301) -0.3860***(0.088) -0.1631***(0.042) -0.4237***(0.061) Loss * Income 0.0244** (0.011) 0.043 (0.031) 0.0165 (0.016) 0.0701**(0.018) Number Observations 19257 2152 9307 7798 Note: t-statistics robust for heteroscedasticity are shown in parentheses. Statistical significance at 10%, 5%, and 1% levels is indicated by * , **, and *** , respectively

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CHAPTER IX

CONCLUSION

Using data on residential home sale transactions as part of corporate relocations for the period of 2003-2014, this study examines the effects of loss aversion on both the listing price and the time on market. I extend and enhance the influential study of Genesove and

Mayer (2001) by introducing a more accurate estimation of loss and equity ratios and by examining the effects on a national level. Additionally, I am able to distinguish between the impact of loss and gain as presented by the study of Bokhari and Geltner (2011). I confirm that loss aversion behavior exists in the U.S. residential housing market. The loss aversion behavior is exhibited by higher listing prices and time on market. Related to time on market, the results of the Cox proportional hazard model show that loss aversion decreases the probability of a house to be sold in a given week while gain has the opposite effect. For a 10 percent loss, the reduction in the probability to sell is 0.45 percent while for 10 percent gain; the increase probability to sell is 1.63 percent.

Furthermore, I examine if the recent financial crisis has altered the effects of loss aversion behavior. The results indicate that after the financial crisis sellers offset the potential losses by increasing the listing price by 10 percent more compared to the overall period. It shows that loss aversion increases in magnitude in times of crisis. Similarly, I

118 examine the effects on time on market. Sellers facing a 10 percent loss can expect a reduction in probability of sale by 0.54 percent during the crisis compared to of 0.05 percent in the pre-crisis period.

I further examine the asymmetrical behavior towards gains and losses as explained by the prospect theory. By comparing the magnitude of the estimates for losses and gains,

I determine that sellers’ behavior is different when faced with prospective gains and losses as observed by Bokhari and Geltner (2011) in commercial real estate transactions. Sellers facing a prospective gain set prices lower than what they otherwise would. The difference in magnitude between the two estimates which is statistically significant (.5689 for loss vs

.3485 for gain) is consistent with the prospect theory where sellers facing a gain price differently relative to a reference point. The results of the Chow statistics tests for statistical difference show that the parameter estimates are statistically different across periods and within the same sample period.

I include a new dimension in the models by introducing income and demographics effects. The new variables enhance the models as exhibited by the higher R2 . The results show that income and family status and gender have a positive effect on Loss aversion and time on market.

In conclusion, the study compliments the research related to loss aversion in the residential housing market. It shows that housing market forces are affected by the behavior of its participants and it is necessary to account for when studying the dynamics of price setting and turnover in the housing market.

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BIBLIOGRAPHY

Anenberg, Elliot. "Loss Aversion, equity contraints and seller behavior in the real estate market." Regional Science and Urban Economics 41 (2011): 67-76.

Anglin, P, R Rutheford and T Springer. "The Trade Off between the Selling Price of Residential Properties and Time-on-the-Market: The Impact of Price Setting." Jpurnal of Real Estate and Economics 26.1 (2003): 95-111.

Bokhari, Sheharyar and David Geltner. "Loss Aversion and Anchoring in Commercial Real Estate Pricing: Empricial Evidence and Price Index Implications." Real Estate Economics 39.4 (2011): 635-670.

De Bondt, Warner, et al. "Behavioral Finance: Quo Vadis?" Journal of Applied Finance (2008): 1-15.

Engelhardt, Gary V. "Nominal Loss Aversion, housing equity contraints, and household mobility: evidence from the Unites States." Journal of Urban Economics 53 (2003): 171-195.

Genesove, David and Christopher J Mayer. "Equtiy and Time to Sale in the Real Estate Market." The American Economic Review 87.3 (1997): 255-269.

Genesove, David and Christopher Mayer. "Loss Aversion and Seller Behavior: Evidence from the Housing Market." The Quarterly Journal of Economics 116.4 (2001): 1233-1260.

Kahneman, D and A Tversky. "Prospect Theory: an analysis of decision under risk." Econometrica 47 (1979): 263-292.

Kahneman, Daniel. "A Psychological Perspective on Economics." Views of Economics From Neighboring Sciences 93.2 (2003): 162-168.

Kumar, D and B Klefsjo. "Proportinal Hazard Model: A Review." Reliability Engineering adn System Safety 44.2 (1994): 177-188.

Lamont, Owen and Jeremy C Stein. "Leverage and House-Price Dynamics in U.S. Cities." RAND Journal of Economics 30.3 (1999): 498-514.

Larsen, J E and W J Park. "Non-Uniform Percentage Brokerage Commissions and Real Estate Market Performance." AREUEA Journal 17.4 (1989): 422-438.

Leung, Tin Cheuk and Kwonk Ping Tsang. "Anchoring and Loss Aversion in the housing market: implications on price dynamics." Chican Economic Review 24.C (2013): 42-54.

Paraschiv, Corina and Regis Chenavaz. "Sellers' and Buyers' Reference Point Dynamics in the Housing Market." Housing Studies 26.3 (2011): 329-352.

Stein, Jeremy C. "Prices and Trading Volume in the Housing Market: A Model with Down- payment Effects." Quarterly Journal of Economics 110 (1995): 379-406.

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Tversky, A and D Kahneman. "Judgement Under Uncertainty: Heuristics and Biases." Science 185.4157 (1974): 1124-1131.

Tversky, Amos and Daniel Kahneman. "Advances in Prospect Theory: Cumulative Representation in Uncertainty." Journal of Risk and Uncertainty 5.4 (1992): 297-323.

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ESSAY 3: INFORMED SELLERS’ BEHAVIOR AND SIGNALS: EXPLORING THE DESIGN OF AN EARLY WARNING SYSTEM FORM HOUSING MARKET STRESS

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INFORMED SELLERS’ BEHAVIOR AND SIGNALS: EXPLORING THE DESIGN OF AN EARLY WARNING SYSTEM FORM HOUSING MARKET STRESS

SVETOSLAVA ALEXANDROVA

ABSTRACT

Neoclassical finance theory does not allow sentiments and preferences to play a role in asset valuation. Efficient markets assume that asset prices reflect all available public information and their values are determined by rational market participants. Cognitive biases are irrational components of market participants’ expectations. As sentiment and heuristics have played a significant role in the past bubbles, it is important to incorporate them in models measuring stress.

Since the most recent financial crisis, research topics such as predicting and detecting stress and creating early warning systems (EWS) have become much more relevant. An EWS is designed as a forward-looking instrument providing anticipatory signals of a crisis. This study aims to design a housing stress index and commence the creation of a comprehensive EWS specifically for the

U.S. housing market. The contribution to the existing literature is constructing a stress index for the housing market and a model that includes both economic factors as well as behavioral variables to explain and detect stress in the housing market. I utilize a private database of transactional home sale data collected as part of corporate relocation transfers for the period of 2003-2014 to construct the behavioral factors. Since a specific index designed to detect stress in the U.S. housing market does not exist, I transform two widely accepted pricing indices- S&P500/Case-Shiller 20-city monthly index and S&P500/Case-Shiller 10-city monthly index to serve as a stress index. I introduce the concept of “informed seller”. Corporate relocation home sale transactions managed

123 by a third party relocation company provide additional housing market information that leads to superior knowledge otherwise not available to other home sellers. I hypothesize that economic signals and home sellers’ behaviors can explain the variability of the housing market stress index.

The preliminary results of the autoregressive models compared to the baseline model are consistent with our expectations. I find that credit availability, delinquency rates and market expectations of the “informed sellers” have statistically significant explanatory power.

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TABLE OF CONTENTS

Page

ABSTRACT.…………………………………………………………………………123

LIST OF TABLES.…………………………………………………………………..127

LIST OF FIGURES.…………………………………………………………………128

CHAPTER

I. INTRODUCTION…………………………………………………………129

II. EARLY WARNING SYSTEM- THEORETICAL FRAMEWORK……...134

2.1 Early Warning Systems-Overview....…………………………….……134

2.2 Construction of Financial Stress Indices...…………………………….138

III. DATA SOURCE AND SUMMARY...……………………………………143

3.1 Data Overview…………………………………………………………143

3.2 Introduction of the “ Informed Seller” Concept……………………….150

IV. CONCEPTUAL DESIGN OF THE HOUSING MARKET EWS………...153

4.1 Measuring Stress in the housing market: Selection of HMSI (housing market stress index)………………………………………………………………..154

4.2 Transformed S&P500/Case Shiller Index- dependent variable……….155

4.2.1 Selection of another benchmark series for comparison…156

4.2.2 Design of Housing Market Stress Index…………..…….157

4.2.3 Levels of Stress………………………………………….165

4.3 Indicators- A rationale for behavioral and signal patterns…………….168

4.4 Empirical Models……………………………………………………...176

V. HYPOTHESES AND ECONOMETRIC FRAMEWORK………………..177

5.1 Hypotheses Formulation……………..…………………………………177

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5.2 Econometric Framework………………………………………………178

5.2.1 Multicollinearity and autocorrelation…………………….178

5.2.2 Optimal Lag Selection……………………………………178

VI. PRELIMINARY RESULTS………………………………………………180

6.1 Descriptive Statistics…………………………………………………..180

6.2 Preliminary Results……………………………………………………182

VII. CONCLUSION……………………………………………………………187

BIBLIOGRAPHY…………………………………………………………………189

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LIST OF TABLES

Table Page Table I. Database Variables ...... 143

Table II. Observation distribution by Metropolitan Areas of Departure ...... 148

Table III. Components of the Chicago Fed National Activity Index Personal Consumption and Housing Series- Source Chicago Federal Reserve ...... 156

Table IV. Variable Legend ...... 165

Table V. Correlation Coefficients ...... 165

Table VI. List of Variables categorized as behavior, signal or mixed ...... 172

Table VII. Descriptive Statistics- 2003:Q1- 2014:Q4 ...... 181

Table VIII. Descriptive Statistics – 2007:Q1- 2009:Q4- Crisis Period ...... 182

Table IX. Results – Base and Parsimonious Models ...... 184

Table X. Results – Base and Parsimonious Models Monthly Stress Index ...... 185

Table XI. Results –Parsimonious Models Aggregate Stress Index : 2003: Q1- 2006: Q4 ...... 186

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LIST OF FIGURES

Figure Page Figure 1. CFNAI-MA3 and Business Cycles...... 141

Figure 2. Reasons for Moving ...... 146

Figure 3. Number of Home Sale Transactions Per Departure State 2003:Q1- 2014:Q4 .149

Figure 4. Top Ten Departure States per Number of Home Sale Transactions ...... 149

Figure 5. Conceptual Design of Housing Market Early Warning System ...... 153

Figure 6. Personal Consumption and Housing Series Levels during recession events ...157

Figure 7. Monthly Stress Series Behavior ...... 162

Figure 8. Aggregate Stress Indices Behavior ...... 163

Figure 9. Monthly Indices Level of Pressure- Standard Deviation Behavior ...... 167

Figure 10. Aggregate Indices Level of Pressure- Standard Deviation Behavior ...... 167

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CHAPTER I

INTRODUCTION

What the recent financial crisis has taught us is that the cointegration between markets exacerbates the spillover effects from instabilities in each one of the individual markets. When the bubble burst in 2006, a chain reaction started, bringing to light the overall fragility and vulnerability of the U.S financial system. The sharp decline in prices followed by the following default in the subprime mortgage market led to devaluation of the Mortgage Backed Securities and related derivatives. The subsequent liquidity and credit crunch caused significant turmoil in the financial markets. Investors’ confidence hit new lows and the security of several large financial institutions and banks was quite uncertain. The effect came full circle when the financial meltdown led to additional downward pressure in the housing market. The increase in foreclosures meant higher supply levels while the struggling financial system and increased uncertainty meant significant drop in demand. By the end of 2008, some of the cities included in Case-Shiller

U.S. National Index had experienced a drop in the housing prices of almost 30 percent which in essence translated to significant housing wealth adjustments. Baker (2008) estimates the loss in wealth to $7 trillion of housing bubble wealth which equates to 50 percent of GDP. He concludes “there is no way that an economy can see a loss of wealth of this magnitude without experiencing very serious financial distress” (Baker, 2008, p.75).

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Various studies show the relationship and importance of real estate health and the connection to other markets. Reinhart and Rogoff (2009) show on a multi-national level various events where the real estate market had a significant role in triggering a bank crisis.

Similarly, Crowe et al. (2011) conclude that more than 46 banking crises in various countries have been preceded by real estate booms and busts and over 65 percent of the boom and bust events have been followed by a banking crisis. Allen and Carletti (2013), on the other hand, develop a two-regime real estate pricing model and determine that speculators’ behavior and subsequent agency problems in lending to them create a bubble in real estate prices. They conclude that the bust of the bubble in the U.S. housing market has had a clear role in the most recent current crisis in several countries besides the U.S. such as Spain and Ireland (p.33).

Subsequently, academic and industry related research shifts focus to increase the ability to predict and identify risk faster so that policy makers have more time to accurately assess the proper policy measures and adjustments. The body of research on EWSs has significantly expanded since the inception of the crisis.

Since crises are infrequent, their predictability is a challenge. The recent financial crisis and its widespread implications has led to heightened interest in identifying better measures of systematic financial stress and constructing ways to predict the overall financial system risk. Various financial stress and condition indices have been developed using different weighting methods and incorporating various indicators.21 The next section will expand on the theoretical framework of EWSs and construction of financial stress indices.

21 For additional information on weighting methods, please refer to Gramlich, et al. (2012) and Illing and Liu (2006)

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Review of the literature shows an absence of a comprehensive early warning model for the U.S. housing market as well as a stress index specifically designed to detect pressure in the housing market, and only a few EWSs for the overall financial market that integrate housing market components. Similarly, after the crisis, several financial conditions indices or financial stress indices such as the Cleveland Financial Stress Index Ire designed to detect increased pressure in the financial system but one specific for the housing market is yet to be developed. In addition, many of the current EWSs focus on the macro and systematic risk ignoring to an extent the “irrationality” of investors and markets.

Therefore, referring to those spare approaches, this study aims to design a housing market stress index and commence the development of a more comprehensive and innovative model for early warning in the real estate markets. The main focus of this study is to establish a stress index or indices to serve as the backbone of the housing market EWS.

I also commence the development of an EWS by introducing a new dimension. My innovative approach is to incorporate not only indicators and signals from macroeconomic conditions that affect the health of the housing market but also identify behaviors of the housing market participants that can incorporate bearish and bullish market perceptions.

The goal of this study is not to present a fully designed EWS, but rather to determine if the signals and behaviors have an explanatory power and can indeed be incorporated into such a system. In future research, I will focus on the overall design and completion of the EWS.

This study utilizes both public economic data and private transactional house sale data. My approach to designing a stress index is to find a representative time series that meets the economic and statistical rationale and allows us to detect pressure in the housing market. I use the S&P500/Case Shiller 20 city monthly price index and S&P500/Case

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Shiller 10 city monthly price index as benchmark indices to transform and design into a stress index by adjusting for inflation and GDP growth and determining the deviation from its continuous and series averages to detect pressure in the market. Through careful consideration and examination of several different transformations of the S&P500/Case

Shiller price indices, I determine four time series that meet this study’s requirements for a stress index.

To determine if behaviors of the market can be incorporated in an EWS, I identify signals and behaviors that I expect to systematically assess and predict housing market risk.

I define signal as an economic measure related to the health of the market and behavior as an impact of an action from a market participant (seller). I introduce the concept of

“informed seller” in the housing market and posit that in combination with certain macroeconomic signals, the behavior of the “informed seller” as a result of his or her anticipation of market health can allow us to identify earlier level of stress in the housing market.

This study uses private transactional data that includes homesale transactions related to relocation moves from the period of 2003-2014 that allows for the observation of such behaviors as loss aversion and regret22. I identify variables from the database and categorize them in three main categories- signals, behavior or mixed. I combine those measures with the economic market indicators such as affordability, liquidity, and unemployment rates. I then perform autoregressive analysis testing the main hypothesis that signals and behaviors can detect stress in the housing market. The preliminary results

22 Loss aversion is based on the Kahneman and Tversky (1971) prospect theory where loss and gains are viewed differently; while regret is based on regret theory (Bell (1987) )

132 of the parsimonious and base models show that signals and behaviors do in fact possess explanatory power.

The rest of the paper is structured as follows: Section 2 discusses the theoretical framework and overall design of an EWS. I do not have a separate section on literature review as I discuss the pertinent studies along with the relevant components of the EWS.

Section 3 focuses on the Data and database construction. Section 4 frames the conceptual design of our Housing market EWS; it outlines the main models and variables and focuses on the design of our stress index. Section 5 formalizes the hypotheses and discusses the econometric framework. Section 6 presents the preliminary empirical results of our study while Section 7 concludes and provides the directions to complete the study as Ill the implications of our preliminary results.

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CHAPTER II

EARLY WARNING SYSTEM –THEORETICAL FRAMEWORK

2.1 Early Warning Systems Overview

An EWS is designed as a forward-looking instrument providing anticipatory signals of a crisis. Its foundation is the causal relationship between factors that drive the crisis and the crisis itself so that systematic risk on macro and micro level can be predicted. The main objective is to alert policy-makers of potential for future crisis and provide time to

“mitigate a potential financial crisis” (Gramlich, Miller et al, 2010, p.451). It is important to present what constitutes systematic financial risk. The Group of Ten (2001) defines financial crisis as an event that will trigger loss in confidence or economic value that can have significant adverse effect on the economy. The stress is a “continuous variable with a spectrum of values, where extreme values are called crisis” (Illing and Liu, 2006, p.244).

In the opening remarks at the Bundesbank German Research Foundation IMF

Conference, Min Zhu, a Deputy Managing Director at the IMF, states that “housing is an essential sector of the economy but also one that has been the source of vulnerabilities and crises” and for which detecting over- valuation and managing housing booms are still a challenge (Min, 2014). The housing market has a unique role as an investment vehicle as

134 well as a conduit of policy through the credit markets. Housing asset bubbles arise for a number of reasons as a combination of various factors. Asset prices can deviate from fundamentals due to speculative behavior. During the recent boom market, lending standards and financial liberalization led to high availability of credit providing easier entry to speculators. Additionally, because of financial product innovations (alternative mortgages) homeowners were able to obtain credit and purchase larger homes overleveraging themselves (Duca et al., 2010). Further, due to the nature of delayed supply adjustments, short-term imbalances between supply and demand lead to overshooting of prices creating bubbles.

Allen and Carletti (2013) posit that the standard valuation principles cannot model real estate prices properly when bubbles are present because real estate prices have a tendency of rising very quickly and dramatically declining in the same rapid fashion. They determine that positive serial correlation between prices, wide regional variations, low interest rates and high availability of credit could lead to a bubble creation. The authors examine the effect of cutting interest rates on the bubble and determine that high loan-to- value in combination of low rates can trigger a bubble. Thus, a macro policy of restrictions on loan-to-value ratios can control the boom and bust cycles. Borio and Lowe (2002) state that rapid growth in credit and rapid increase in asset prices on its own may not pose a big threat but when those imbalances are combined together, they can lead to significant financial instabilities. The focus should not be on the presence of a bubble but rather on detecting those imbalances and responding to them before a crisis occurs. The authors suggest that a monetary policy of increasing interest rates may “prick” the asset bubble and preserve financial stability. As a result, I argue that an early warning model that explains

135 the relationship between risk drivers and risk itself in the housing market is necessary to allow policy makers to detect and respond to pressures much earlier.

Currently very few EWSs consider the properties of the housing market but there are quite a few to address bank or currency crises. One of the first studies related to modelling crisis focuses on currency crisis as Frankel and Rose (1996) determine that an

EWS can be constructed to predict the probability of a currency using probit analysis. The probit or logit models use a crisis indicator as a binary variable and use explanatory variables to determine their individual contribution to the crisis. The approach has been used in several studies related to systematic banking crisis in both emerging and developed markets.

Subsequently, Kaminski and Reinhardt (1999) design EWS for currency and banking crisis using a different approach- signal extraction approach. They determine that banking crises precede currency crises and that weak fundamentals are the reasons of both crises. As a result, the authors conclude that early warning signs of both crises exist and can be determined. They further develop the methodology related to signal extraction in

Goldstein, Kaminsky and Reinhart (2000). Their approach utilizes the fact that certain variables during the crisis and prior to the crisis exhibit abnormal behavior and when certain levels are reached those variables signal stress in the financial system. Of course, the key is determining what level constitutes a warning signal. They determine that the level of stress is the one that minimizes the number of false positive indications of stress for the period of two years prior to a crisis (Type I vs Type II errors).

Oet, Bianco, Gramlich and Ong (2013) create a new Systematic Assessment of

Financial Environment (SAFE) EWS that allows the supervisors to monitor information

136 coming from the largest bank holding companies to anticipate the buildup of macro stress in the financial markets. The SAFE EWS had a set of short- and long- term forecasting models to provide flexibility to the policymakers in their approach. The contribution of this study is the incorporation of “both microprudential and macroprudential perspectives, as well as structural characteristics of the financial system and a feedback-amplification mechanism” (Oet et al., 2013, p.4511). They utilize the Cleveland Financial Stress Index as measure of stress and optimally lagged variables constructed as imbalances to complete the model.

Dreger and Kholodilin (2011) develop an EWS to predict housing bubbles for 12

OECD countries for the period 1969:Q1 to 2009:Q4. They utilize the signal approach where indicator variables are de-trended using Hodrick-Prescott (HP) filter and standardize

(divided by the country specific deviation). They identify the optimal threshold level for each variable. If the variable exceeds the threshold, it indicates the existence of a bubble.

A composite signal series is computed using weighted average of the squared accuracy coefficients. They also utilize probit and logit model to determine speculative bubbles.

They use Quadratic Probability Score to determine the predictive power of each of the models- the lower the score the better. They conclude that logit and probit models have a better predictive power. This research differs from the approach taken in the Dreger and

Kholodilin’s study where I aim to create a continuous measure of stress for the U.S. market incorporating signals as well as behaviors.

Virtually all of the studies related to predicting financial distress stress the importance of the selections of proper stress index measure, indicators or drivers of risk in order to construct a model that can forecast the inception of a crisis. The next section

137 focuses on the construction of financial stress indices and discusses some of the widely used FSIs in the studies related to stress warning systems.

2.2 Construction of Financial Stress Indices

In order to construct an EWS, a dependent variable that measures stress is needed.

Since no stress index is available specifically for the housing market for benchmarking, I need to identify an index that I can analyze the performance of the EWS. This section discusses several of the Financial Stress Indices (FSIs) that have been constructed to measure the level of frictions in the economy.

The seminal paper of Illing and Liu (2006) has paved the foundation of this line of research. The authors develop a daily financial stress index for the Canadian financial system by introducing several design approaches. First, the authors determine the events that are considered stressful via Bank of Canada internal survey. Second, they identify what indicators would be included in the index and the weighting method. The authors examine three main categories of variables- standard, refined and GARCH variables with various weighing techniques. They compare the Type I and Type II errors to determine performance. Any deviation from the historical mean is determined to indicate a stress event.

Caldarelli, Elekdag and Lall (2011), on the other hand, develop a monthly FSI for

17 countries using the aggregation method. They identify financial stress episodes as extreme value of the composite FSI using variance- weighted average of three sub-indices related to the banking, equity and foreign exchange markets. Variation of more than one standard deviation from the trend is considered a stressful event. The authors identify the trend using Hodrick-Prescott (HP) filter. Caldarelli et al. conclude that ‘ a rapid expansion

138 of credit, run up in house prices, and large borrowings by the corporate and household sectors all contribute to higher stress levels” (pg. 94).

Using eleven daily financial market indicators, Hakkio and Keeton (2009) construct the Federal Reserve Bank of Kansas City monthly Financial Stress Index (KCFSI) using a different approach- principal component analysis. The authors identify the five key features of financial stress: 1) increased uncertainty about fundamental value of assets; 2) increased uncertainty about behavior of other investors; 3) increased asymmetry of information; 4) decreased willingness to hold risky assets (flight to quality); and 5) decreased willingness to hold illiquid assets (flight to liquidity). Each of the variables included in the index has to exhibit at least one of the five features of stress. Since all variables capture some level of stress, they will tend to move together as Ill as change without presence of stress. As a result, they utilize principal component method to extract the factor responsible for stress after standardizing each one of the variables. The weights of each raw variable are determined based on their contribution towards the first component. Similarly, Kliesen and

Smith (2010) develop the St. Louis Federal Financial Stress Index from 18 weekly data series to measure financial stress in the market. The data series are related to interest rates, yield spreads and several other indicators. The higher frequency allows for the index to measure rapid changes in the market but it comes with higher volatility and noise. Zero level indicates normal market while values below zero indicate lower level of stress and above zero values indicate above average financial market stress.

Oet, Eiben, Gramlich and Ong (2011) develop the Cleveland Financial Stress Index

(CFSI) by integrating 11 daily financial market indicators from four sectors- debt, equity, foreign exchange and banking markets. Each indicator is transformed and normalized by

139 the corresponding empirical cumulative density function (CDF). Time-varying credit weights proportional to the flows of funds in each of the four sectors are used to create the aggregate indicator.

Another index related to financial activity is the Chicago Financial National

Activity Index (CFNAI). The goal of the index is to provide indication of turning points and inflationary pressure. The index is constructed through principal component analysis of 85 different indicators grouped in four categories; production and income; employment, unemployment and hours; personal consumption and housing; and sales, orders and inventories. The methodological approach is very similar to James Stock and Watson work on predicting inflations23. The index is constructed to have an average of zero and the standard deviation of 1. A zero value of the index indicates expansion towards the trend while negative value shows below average growth and subsequently positive values indicates above average growth. I am particularly interested in the index because one of the categories is related to the personal consumption and housing which I incorporate as a benchmark index against this study housing market index. The index has identified thresholds to indicate the likelihood of a recession as well as likelihood of increased inflation (-0.7 is the threshold of increased likelihood of recession). Brave and Butters

(2010) examine the performance to the CFNAI-MA3 index (the 3 month moving average of the index of the CFNAI) as related to the economic recessions as identified by the

National Bureau of Economic Research (NBER)24. The authors confirm that CFNAI-MA3 perform very well in predicting recessions. For example, the March 2008 release of CFNAI indicated that the recent recession has started in December of 2007 prior to the

23 The information on the construction is obtained from www.chicagofed.org/cfnai 24 For the period of 1967-2010, NBER has identified seven economic recessions.

140 announcement by NBER (pg.2). Figure shows the performance of the index against the official periods of recession (shaded areas). A CFNAI-MA3 value below –0.70 following a period of economic expansion indicates an increasing likelihood that a recession has begun while a value above –0.70 following a period of economic contraction indicates an increasing likelihood that a recession has ended. A value above +0.20 following a period of economic contraction indicates a significant likelihood that a recession has ended. I further focus on the consumption and housing component in the section related to the selection of housing stress index.

Figure 1. CFNAI-MA3 and Business Cycles.

Source: Chicago Federal Reserve

Various other financial indices have also been constructed for different countries using similar approaches. Yiu, Ho and Jin (2010) construct a FSI for Hong Kong using aggregation method and 6 underlying variables. Hollo, Kremer, Lo Duca (2012) construct a Composite Indicator of Systematic Stress (CISS). They pioneer a new approach of construction by integration of basic portfolio theory. Their index includes five market specific sub-indices created from 15 financial stress measures. This approach allows for the aggregation to take into consideration the time-varying nature of the cross correlation

141 between the sub-components. Thus, “ CISS puts relatively more weight on situations in which stress prevails in several market segments at the same time, capturing the idea that financial stress is more systematic and thus more dangerous for the economy as a whole if financial instability spreads more widely across the whole financial system” (Hollo et al.,2012, p.1). They use threshold VAR (TVAR) model to determine a crisis level of the

CISS FSI where the real economic activity exhibits distress. They determine that the TVAR models allow them to examine stress in different regimes. The results show a significant reaction in high regime of stress compared to low regimes. They conclude that the reaction patterns of CISS show that when the economy is hit by a large shock it can enter into a vicious spiral when economic and financial stresses reinforce each other.

It is very evident from the studies that a housing market specific index has not been designed and established. In this study, I aim to focus on such a challenging task and determine different ways to construct a housing market index that can detect pressure in the market.

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CHAPTER III

DATA SOURCE AND SUMMARY

3.1 Data Overview

This study utilizes a private database of homesale transactions gathered as part of corporate relocation activity. Table I presents a list of pertinent variables from the database.

Data is gathered on each transaction as part of the milestone phases in a home sale: listing, offer to employee, sale to outside buyer. For each of those data points the proper contracts/agreements have been collected – listing agreement, contract of sale, inspection etc. – that allow us to gather the listing date, closing date, price etc. I use those data points to construct the signals and behaviors variables.

Table I Database Variables

Variable

Original Price paid for Home By Transferee Price RMC sold the property to Outside Buyer (Final Sales price)

Date property sold to Outside buyer

Most probable sale price- average of 2 BMAs( Broker Market Assessment of Property Market value- 3-6 months in the future)

Listing price

Date of Listing

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Date offer is presented to the employee Date of acceptance of the offer presented to the employee Acquisition Price ( Price RMC pays to transferee to acquire the property), it is referred to sales price in our study Proration Date ( Date RMC acquires property from transferee) Transferee equity in property at time of acquisition ( EQ= Acquisition price - Mortgage Balance – Prorated Interest + Escrow- Property Taxes – Homeowner association dues -Repairs & Concessions

The second phase in the sampling process is to review each observation to ensure that no missing data. I narrow down the database by excluding any relocation transfers that have not been accepted and homesale transactions that have not been carried through. I define a homesale transaction where two-sale transaction is executed as defined by the IRS guidelines25.

The original sample size of all homesale transactions in the private database from

2001 to Q4 2014 is 66,376 home sale transactions – 63,690 accepted transfer and 2,686 rejected the transfer. In order to determine which month and year the observations fall under, I have to discuss which reference date should be considered. Every homesale transaction starts with the listing date when the property formerly enters the market and ends with the sales date when the buyer takes possession of the property as part of the new sale. I posit that at time of listing, the seller (relocating employee) determines a strategy based on his or her perception of the housing market, so the reference date used to determine which month and year the observation falls under is the listing date.

25 IRS agreement with two sale characterization: Rev. Ruls. 72-339 and 2005-74

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Because of very small sample size and data integrity issues, I drop the years 2001-

2002 which does not result in a significant loss of observation. I drop any observations where data related to the variables needed to include in our model are missing. The final database contains 52,197 homesale transactions for the period 2003:Q1 to 2014:Q4 based on the listing date of the transaction.

The 2012- 2013 Current Population Survey (CPS) shows that between 2012 and

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Figure 2. Reasons for Moving

The database captures sales data across the U.S. Table represents the top 50 departure metropolitan areas of our private database. The S&P/Case-Shiller 20-City

Composite Home Price Index measures the value of residential real estate in 20 major U.S. metropolitan areas (MSA): Atlanta, Boston, Charlotte, Chicago, Cleveland, Dallas,

Denver, Detroit, Las Vegas, Los Angeles, Miami, Minneapolis, New York, Phoenix,

Portland, San Diego, San Francisco, Seattle, Tampa and Washington, D.C. The highlighted rows correspond to an area included in the Case-Shiller Home Price Index. It is apparent that the private database has a good representation in the areas covered by the Home Price

Index thus home sale transaction detail information from those areas can be used to indicate the health of the market. Additionally, the S&P/Case-Shiller 10-City Composite Home

Price Index measures the value of the single homes in 10 major U.S. MSAs: Boston,

Chicago, Denver, Las Vegas, Los Angeles, Miami, New York, San Diego, San Francisco and Washington, D.C. I select two different indices due to the length of the time span each index covers. The S&P/Case-Shiller 20-City Composite Home Price Index commences in

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January 2000 while the S&P/Case-Shiller 10-City Composite Home Price Index commences in January of 1987. In order for us to construct a comprehensive EWS, I need to be able to utilize a longer time series. Additionally, due to the different reference points, the two indices can have different values for the same time period. The ability to construct stress index based on two time series will also allow me to test if the EWS depends on the choice of stress index in future research.

Additionally, Figure 3 shows the distribution of home sale transactions per state.

Several of the states that have exhibited significant price fluctuations are well represented in the database in addition to states that were not as affected by the recent housing market downturn. Figure shows in more detail the top ten states and the number of transactions for each state. States such as Texas, Ohio, California, Illinois, Florida and Michigan that suffered significant price adjustments as a result of the crisis are well represented allowing us to be able extract information in regards to signaling stress. This is also important because it shows that our database is not biased towards observations in areas of high distress.

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Table II Observation distribution by Metropolitan Areas of Departure

Table below shows the top 50 Metropolitan Departure Areas and the number of observations in the database. The highlighted rows correspond to the metropolitan area included in the S&P/Case-Shiller 20-City Composite Home Price Index. We display the 20 city since it already contains the 10 city MSAs.

Total Metropolitan Total Number of Metropolitan Number of Departure Area Observation Departure Area Observation TX Houston 584 IL Moline 134 IL Chicago 555 FL Jacksonville 116

OH Cincinnati 548 NV Las Vegas 113 GA Atlanta 373 OH Cleveland 110 MN Minneapolis 331 IA Waterloo 102 CA San TX Austin 319 101 Francisco TX Dallas 308 TX El Paso 100

NC Charlotte 292 IA Dubuque 95 KS Wichita 286 FL Miami 94 MO St. Louis 258 IA Davenport 91

IN Indianapolis 205 MA Boston 91 CA Los Angeles 200 AZ Mesa 89 TX San Antonio 194 LA New Orleans 88

NE Omaha 187 IA Des Moines 86 MN Saint Paul 184 IA Bettendorf 84 AZ Phoenix 177 KY Louisville 84

CO Colorado Springs 164 FL Orlando 83 OR Portland 163 NM Albuquerque 80 WA Seattle 159 TN Nashville 80

OH Columbus 154 MI Detroit 78 CA San Diego 148 CA Long Beach 77 NC Raleigh 141 OH Mason 75

CO Denver 139 VA Richmond 74 DC Washington 138 IA, Cedar Rapids 71 FL Tampa 136 TX Plano 70

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Figure 3. Number of Home Sale Transactions Per Departure State 2003:Q1- 2014:Q4

Figure 4. Top Ten Departure States per Number of Home Sale Transactions

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3.2 Introduction of the “Informed Seller” Concept

Many of the studies related to constructing early warning systems or financial indices, indicate that the home sale prices are important transmission mechanisms for information that is not carried by other indicators in the overall financial system and as such need to be included as an indicator for stress. However, housing pricing data especially on transactional level is very difficult to compile or access. The unique private database utilized in this study has two very important characteristics. On one hand, it provides transactional aggregate home sale data covering all states in the U.S and on the other hand, allows us to examine the behavior of very unique market participants.

Relocating employees are guided by professionals who are familiar with the real estate market and follow well-disciplined processes related to the listing and selling of a home on a regional and zip code level.

Generally, when homeowners decide to sell a house, they decide either to sell independently or hire an agent to help in the selling process. The choice of a selling agent is governed by many deciding factors, which are out of the scope of this study, but one of the main reasons why a homeowner hires an agent is to gain access to valuable information to facilitate the sale of their house. In general, the real estate market is characterized by incomplete information and higher search costs (Quan and Quigley, 1991) and due to the illiquidity of the market, price adjustments related to new information are much slower.

Kurlat and Stroebel (2014) argue that information asymmetry in the residential real estate market is exhibited by sellers having superior information to buyers related to the particular property and additionally by sellers and buyers may have “information advantage” over their peers (Kurlat and Stroebel, 2014). For example, local real estate agents may be aware

150 of certain projects that can increase the value of the properties in a particular area; local buyers have better information related to buyers coming from different areas or states. The primary role of a real estate agent is to reduce the search cost of the seller and find a buyer for the property as well as to assist the seller in determining a proper list price (Bagnoli and

Khanna, 1991). Unfortunately, agents may exploit the information asymmetry created by their superior knowledge of the local housing market. Levitt and Syverson (2008) study the market distortions when the agents are better informed. They find that properties owned by real estate agents sell for 3.5 percent higher and stay longer on the market – 9.5 days compared to other homes. They posit that agents will urge their clients to sell faster and quicker do due to “distorted incentives” because they receive a small portion of the additional profit when houses sell higher than the original listing prices. The authors preset one possible way to counteract such market distortion is by homeowners hiring independent appraiser to determine the value of the property vs. depending on the agent for such evaluations.

In a home sale transaction as part of relocation, I also introduce another intermediary, the relocation management company (RMC) assisting the relocating employee in the transaction. I posit that the introduction of an intermediary that specializes in a home sale transaction on a national level increases the level of information presented to the transferee to make an informed decision regarding the choice of agent and choice of listing price and reduces the information asymmetry and possible market distortion due to agency problems. The RMC acts in the best interest of the relocating employee thus providing an objective view of the housing market information and value of the property.

Additionally, the determination of listing price is based on the type of a homesale program

151 offered by the employer- an average of two Broker Market Analyses which is a forecasting mechanism for determining most probable sales price or an average of two appraisals which determines the proper value of the asset. Non-relocation housing transactions do not include forecasting mechanism in the determination of value of the property. Being part of a homesale relocation transaction allows for additional information otherwise not available to average home sellers and their agents. As a result, I introduce the concept of an

“informed seller”. Informed sellers, similarly to informed traders, possess superior knowledge not only of the market but also the quality of the asset, i.e. home, and the true value of that asset and they have additional tools to properly set a reservation price.

Additionally, the profile of the typical relocating employee also contributes to the concept.

Generally, transferees are well- educated, mid-level and up management and have relocated multiple times thus the additional experience of participating in housing markets in different locations increases their knowledge and minimizes the information asymmetry compared to their peers.

It is assumed that studying the behavior of those informed sellers who possess superior knowledge will provide additional information related to the health of the housing market and will lay the foundation for incorporating behavioral aspects as indicators for predicting stress. In addition, I posit that “informed seller’s” superior understanding of the market will allow detecting stress in the market earlier than from observation of un- informed participants.

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CHAPTER IV

CONCEPTUAL DESIGN OF THE HOUSING MARKET EWS

The three basic elements of an EWS are “a measure of financial stress, drivers of risk, and a risk model that combined both” (Oet et al., 2013, p. 4511). Figure presents the conceptual design of our early warning system. First an index needs to be identified or developed that will allow us to identify levels of stress. Second, indicators that can assess and predict stress in the housing market need to be selected and their usefulness in the predicting stressful events is to be validated. Finally, an early warning model is obtained.

In the next subsections, I will address the selection of signals and behaviors as stress indicators as well as the identification and transformation of the Housing Market Stress

Index to design the early warning model.

Figure 5. Conceptual Design of Housing Market Early Warning System

Signals/behavior patterns Predict Housing Market Stress Index Early Warning System ( EWS)

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4.1 Measuring Stress in the housing market: Selection of HMSI (housing market stress index)

In order to design a robust early warning system, the proper index that measures financial stress must be either constructed or identified. As discussed in the previous section, in the recent years, various Financial Condition Indices have been created and constructed; however, to the best of my knowledge, one specifically for the housing market is yet to be created. Some of those indices contain a housing market component as part of the overall index but one specifically constructed to measure the financial stability of the housing market has not yet been designed. In this study, I do not aim to construct such index, so I will identify a suitable proxy from the existing housing price indices. I decide to utilize one of the housing price indices for two main reasons. In addition to supply and demand forces, prices only incorporate the market participant sentiments about the health of the market and its future performance. Since the ultimate goal is to design an EWS that models psychological factors as well as economic factor, I search for a proxy that could capture both. The changes in the price index show not only pressure from an economic point of view but also can capture the market participants’ expectations of the market. The most widely used indices related to housing market pricing are- S&P500/Case Shiller

House Indices and the OFHEO constructed by the Office of Federal Housing Enterprise

Oversight. I decide to utilize the Case Shiller Monthly Indices vs OFHEO Index for two main reasons. Case Shiller Index includes all home transactions while OFHEO includes transactions with conforming mortgages and since I aim to predict stress, I would like to incorporate the risk related to sub-prime mortgage market. Additionally, the market

154 capitalization weighting technique utilized by Case and Shiller give larger weight to the markets with higher home prices, which generally are subject to larger swings (Hatzius,

2008).

There are various methods of transforming an existing index to a stress index. One way is to examine the fluctuations of the index – either daily or monthly- to an average or trend and identify anything over 1-2.5 standard deviations as an indicator of stress. Another approach is to determine an appropriate benchmark and examine the changes in the index within that interval benchmark. A third approach is to use the empirical cumulative distribution function (CDF) to transform the raw price index and compare its fluctuations to another time series well recognized to predict stress.

4.2 Transformed S&P500/Case Shiller Index- dependent variable

Since there is no stress index specifically designed for the housing market, I need to either construct or locate a reasonable proxy. Thus, I need to establish if the transformed

S&P500/Case Shiller housing Index is a good indicator of stress in the housing market.

There are two approaches to determine if the index reflects stress events. One approach is to identify crisis events in the housing market and examine the behavior of the index and determine if the index goes above or below its trend. The other approach is to examine the behavior of the index related to another time series data index that is considered to be a measure of the stress in the overall financial market. I incorporate both approaches to determine if the transformed index is a good indicator of stress.

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4.2.1 Selection of another benchmark series for comparison

First, I identify another data series that measures stress or financial activity related to the stability of the overall economy, then I observe the behavior of the transformed

S&P500/Case Shiller index in time of crises. There are several indices that have been developed over the recent years to help regulators and the market to indicate some type of pressure or stress on the overall market or financial system such as Cleveland FSI. Kansas

FSI and Chicago Fed National Activity Index (CFNAI). There is not a well-established financial stress index specifically derived or created to measure stress in the housing market. As a result, I need to identify an index related to the housing market to serve as a benchmark proxy. The CFNAI has a component series related to Consumer Consumption and Housing Market so I identify that series to perform series of correlation tests.26 The

Chicago Fed National Activity Index Personal Consumption and Housing is derived by using principal component analysis. The table below specifies the individual data points used to transform and derive the component.

Table III Components of the Chicago Fed National Activity Index Personal Consumption and Housing Series- Source Chicago Federal Reserve

26 Correlation analysis has been performed with the Cleveland Financial Stress index, and Kansas FSI; results are available upon request.

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Figure 6 presents the performance of the component related to several crises in the housing market—the 2005-2006 Housing bubble burst & the recent financial crisis of

2007-2008. It is evident that the series reacts appropriately to the crisis by declining in level.

Figure 6. Personal Consumption and Housing Series Levels during recession events

In order to determine if the selection of Housing Stress Index proxied by the transformed Case Shiller indices, is appropriate, I will analyze each of our stress indices against the Consumer and Housing Component Series (CHF).

4.2.2 Design of Housing Market Stress Index

The design of the stress index has proven to be quite challenging. Maybe that is one of the reasons why there is not a well-accepted comprehensive housing market stress index developed as of yet. In my preliminary work, I transformed the S&P500/Case Shiller 20- city Monthly index by taking the actual index and construct a new index using a CDF

157 transformation. I felt that the index did not provide enough indication of stress and still possessed many of the pricing index characteristics to allow the detection of a market disequilibrium.

Thus, I focus on the design and selection of a new index that can capture the deviations and allow me to measure pressure. The main selection factors are related to the behavior of the new series and its correlation to our benchmark series- the housing component of the CFNAI Index. To design an early warning system (EWS), I need to be able to observe and detect deviations in the market from the equilibrium to be able to forecast pressure. I evaluate each of the constructed series against those factors to determine a stress index that can serve as the basis of the EWS. I construct the stress indices by utilizing S&P500/Case Shiller 20-city Monthly index and the S&P500/Case Shiller 10- city Monthly index as a base. Those two well-established house price time series are often observed to determine housing market health and can be utilized to extract information related to the market perceptions of health. Thus, deviation from the equilibrium can be viewed as signs of pressure and signal market stress.

In order to determine the best representative series that matches our theory of stress,

I decide on the following systematic approach. First, I want to extract the true pressure of the market from the base series. I follow the economic rationale that prices continue to rise with inflation thus, I need to strip the inflation from our series so that I can observe the price movements due to market behaviors. Thus, I deflate the two indices by the CPI monthly index. I like to examine further, how the market reacts to its own pressure on prices and excitations and determine the deviation that will allow measuring the disequilibrium. Expanding economy can bring natural increase in prices due to the demand

158 and supply forces. Thus, I want to be able to minimize the impact of the growth of the economy on our housing prices so I can observe market pressure. I adjust our series for

GDP growth and overall GDP levels.

Secondly, I examine different ways that the deflated and adjusted for GDP growth series can express pressure by employing various statistical avenues in the transformations.

I decide on utilizing several different statistical approaches to select the best series that can allow for extracting the pressure- structural model transformations, moving average transformation and standardization. I further evaluate the speed of change and intensity of change by exploring the behavior of the 1st and 2nd derivative of the series. The main goal is to capture the notion of stress and be able to measure the deviation from the equilibrium.

I create time series for each of these approaches so I can evaluate against the selection criteria for a stress index. I describe the transformation process in the text below.

I transform the two series via a structural model methodology by utilizing an unobserved component model (UCM). UCM allows the analysis and forecasting of equally spaced univariate data. The model returns a series that is decomposed into components for trend, seasonality, cyclicality and regressive factors due to prediction. Harvey (1989) explains in detail the UCM model and its versatility- a combination of ARIMA and smoothing component methodologies. I utilize two year and three-year data to predict the next 12 data points. I compare the behavior of the constructed series against the main criteria for selection: 1) evidence of deviation during crisis periods as well as 2) behavior against the benchmark- Housing and Personal Consumption component of CFNAI (CHF).

The new constructed time series via UCM provided for a very good fit to the original data not allowing me to observe the variations due to pressure.

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The second set of transformations is related to moving average series. I explore different moving averages deviations for 12 month, 18 month, 24 month, 36 month and 48 month moving average series of both the 10 city and 20 city S&P500/Case Shiller Monthly

Indices. I further explore absolute values of the series against the level of deviations of the moving averages from the actual series to address the challenges related to negative values of the stress index. The moving averages series had a low correlation with the benchmark but most importantly the design does not provide the information I need related to deviations. The concern is the smoothing factors of the moving averages as a proxy for trend.

I further examine the speed and momentum of the series by constructing the 1st and

2nd derivative of the transformed series. The newly constructed indices did not provide even visual evidence for deviations during the crisis. I need to be able to show pressure built up, the actual bursting of the bubble, the crisis period and the following consolidation; transformation of the series would not be able to provide us with the visibility.

I complete the transformation construction by designing a standardized aggregate series and monthly changes series from deflated and adjusted for GDP base indices. I determine the monthly changes of the deflated 10 city- and 20 city- time series and subtract the GDP growth (linearly interpolated from the annual percentage growth change). To address the concern of how well monthly changes and deviations contain information related to pressure in the market, I construct a standardized aggregate stress index for each of the two base time series by assigning a value of 100 for the first month of commencement of the base series. I first standardize the deflated 10- city and 20 city- indices and then subtract the series from the standardized real quarterly GDP values.

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In order to calculate the pressure of the market, I determine the delta between the new series and its series average to examine the deviations. For the monthly indices I utilize the series average and for the aggregate series I calculate the cumulative continuous average and also examine the deviations against it.

In the last phase, I compare each of the constructed series against the benchmark series from the Chicago Financial Activity Index. I further examine the behavior of each of the series during the housing crisis and the financial crisis. I determine that four of the series meet all of the outlined selection criteria related to the new stress index as described in the beginning of the section. I present the results of the series below. The evaluation results for the other series are available upon request.

 Monthly Stress Index (10 city)- the deviation of the standardized monthly

change of the deflated 10 City index adjusted for GDP growth rate from its

series average

 Monthly Stress Index (20 city)- the deviation of the standardized monthly

change of the deflated 20 City index adjusted for GDP growth rate from its

series average

 Aggregate Stress Index ( 10 City)- the deviation of the standardized deflated

10 city index adjusted for GDP level from its continuous average

 Aggregate Stress Index (20 City)- the deviation of the standardized deflated

20 city index adjusted for GDP level from its continuous average

The graphs below show the behavior of the four new stress indices during the crisis.

Both series show increasing deviations prior to the housing bubble burst in 2006. It is very evident in both aggregate 10 city and 20 city indices the steep increase in deviation prior

161 to the bubble. In the monthly changes graphs, more noise can be seen but the increase of deviation prior to the housing crisis can still be detected. One concern I need to address in future research is how to handle the negative levels of oust stress index. Intuitively, the expectation is to see positive levels to determine increase pressure. In the negative levels, pressure can still manifest itself but the speed of adjustment should be smaller. In future work, I intend to create a combined index to be able to incorporate both positive and negative levels in future studies.

Figure 7. Monthly Stress Series Behavior

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Figure 8. Aggregate Stress Indices Behavior

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In Table IV and Table V the descriptions of all the series and the correlations are shown. I want to examine the correlation with the previously constructed index utilizing

CDF as comparison to previous work. Very high correlations between the 10-city aggregate stress index and the Chicago Component Index are observed which is one of the criteria to select the stress index. I also see high correlation between the two aggregate indices which is also significant. Interestingly, the monthly indices, however, are not correlated (not significant 0.1150). As previously mentioned, due to different reference points, the monthly values of the two indices can be different. The correlations with the index transformed via CDF with the monthly stress indices are not significant while high correlations with the aggregate indices are observed. This is related to the construction and design of the indices- the monthly stress index is designed as a monthly change standardized and adjusted for inflation and GDP growth, while the aggregate indices are standardized as an overall series thus much closer to the CDF transformed Case Shiller 20 city Index for the preliminary work presented in the proposal.

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Table IV Variable Legend

Variable Definition CSHI_T Case Shiller 20- city Monthly index transformed using CDF CHF Personal Consumer & Housing Category Component- level (monthly) Monthly Stress Index (10 City) Deviation of the standardized monthly change of the deflated 10 City index adjusted for GDP growth rate from its series average Monthly Stress Index(20 City) Deviation of the standardized monthly change of the deflated 20 City index adjusted for GDP growth rate from its series average Aggregate Stress Index (10 city) Deviation of the standardized deflated 10 city index adjusted for GDP level from its continuous average Aggregate Stress Index (20 City) Deviation of the standardized deflated 20 city index adjusted for GDP level from its continuous average

Table V Correlation Coefficients

CHF Aggregate (10City) Monthly (10 Aggregate Monthly (20 CSHI_T City) (20City) City) CHF 1.00

Aggregate(10City) 0.4294*** 1.00

Monthly(10city) 0.4456*** 0.1479** 1.00

Aggregate(20city) 0.8425*** 0.9489*** 0.2937*** 1.00

Monthly(20city) 0.5321*** 0.1150 0.9925*** 0.2868*** 1.00

CSHI_T 0.5646*** 0.8002*** -0.0159 0.7542*** 0.0056 1.00

4.2.3 Levels of Stress

The next step in the analysis is to determine the level of stress/pressure in the market so that can be incorporated into a future EWS. This is another challenging task since there is not a well-defined and agreed upon stress measure for the housing market that I can relate to. The level of stress is determined as a deviation from the equilibrium or its long- term trend. One way to examine different levels of pressure is to determine a threshold of the stress level. Once the index goes over the threshold, I can conclude that the market is

165 exhibiting pressure. A different way is by examining the standard deviation of the delta.

For this study, I utilize the standard deviation approach. In future research, I plan on utilizing an error correction model to determine the speed of adjustment to the actual long term trend as an indicator for stress vs. the level of stress determined by standard deviations.

I identify any deviations above 1 standard deviation27 as “yellow” alert and anything over 1.5 standard deviations as “red” alert. In the graphs below I show a graphical representation of the behavior of the stress index in number of standard deviations to substantiate the choice of level. The housing market had shown some signs of slow down at the end of Q4 2005 but the bubble had burst sometime mid-August of 2006. Thus, I expect to see elevated stress levels around those time frames.

Figure 9 and Figure 10 show the number of standard deviations as the difference between the index and its series average for the monthly series and cumulative average for the aggregate series. The graphs show elevated “ yellow” level around 1 standard deviation and “ red “ level for over 1.5 deviations. Just before the housing crisis, July 2005, the level of delta goes over 1 standard deviation and continues to rise until February 2006 where the deviation continues at a decreasing rate. It is also important to note that high deviations exist in the negative levels too, thus it is important for us to examine the speed of adjustment.

27 Borio and Lowe (2002) use 1 standard deviation from the mean when analyzing the indictor gaps over a period centered around the banking crisis they are modeling to predict.

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Figure 9. Monthly Indices Level of Pressure- Standard Deviation Behavior

Figure 10. Aggregate Indices Level of Pressure- Standard Deviation Behavior

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4.3 Indicators- A rationale for behavioral and signal patterns

To properly design an early warning framework, it is vital to include potential leading indicators with the ability to seize the existence of imbalances within the financial system that may lead to a stress event in the housing market. Following the conceptual model of stress in the housing market, evidence from the recent crisis and literature, two sets of variables are included: signals and behaviors. Additionally, I posit that behavioral aspects of the market participants can also show signs of stress early enough to serve as a warning signal. Traditionally, the behavior of the market participants is modeled based on rational decision-making based on the use of all available information (De Bondt et al.,

2008). Thus, neoclassical economic theory posits that rational sellers and rational buyers will look at the current market price in order to determine a value of the property. Studies, however, show that psychological biases may affect the decision making process from both the seller’s and buyer’s point of view. Case and Shiller (1988) determine that expectations heavily influence the price a buyer will pay which contradicts the efficient market theory.

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In his book “Irrational Exuberance” 28, Shiller puts forward an argument that the stock markets are overvalued. In a subsequent edition in 2005, he also determines that a real estate bubble not only exists but it is ready to burst.

Behavioral finance is pioneered by Tversky and Kahneman (1974) and Kahneman and Tversky (1979) when they introduce the concept of prospect theory. As an alternative to expected utility, prospect theory incorporates the psychological aspects in choice behavior. Individuals determine their choice by weighing the loss and gains relative to a reference point. Additionally, the decreasing marginal sensitivity states that the individual decision maker will value additional unit of gain or loss less than the previous gain or loss.

Last, decision makers exhibit loss aversion, i.e. they are more sensitive to losses than gains.29 In the world of financial markets, loss aversion translates into inconsistency of behavior towards risk. Individuals may assume risk to protect sure losses while avoiding risk to protect existing wealth (De Bondt et al., 2008). In the housing marker framework,

Genesove and Mayer (2011) publish the influential paper on loss aversion in the Boston area in the 1990s. The study finds that homeowners are loss averse relative to the original price they paid for the property; as a result, they tend to list the property for above market price, which leads to longer time on the market.

Two other heuristics are central to this study: narrow framing and regret aversion theory. Mental accounting is developed by Thaler (1985) and examines how individuals categorize financial and economic outcomes. Shefrin and Thaler (1988) posit that decision makers assign a one of the following three categories to wealth: current income, current

28 The term” irrational exuberance’ is actually coined by Alan Greenspan 29 Further studies by Tversky and Kahneman find that on average a loss has twice as much psychological impact as an equivalent gain.

169 wealth and future income. Individuals tend to consume more from current wealth than future wealth categories. One of the consequences of that behavior is so-called narrow framing- where decision makers treat new risks differently than existing ones (De Bondt et al., 2008). It translates into inconsistency in the behavior of the market participants; in one situation, they can be risk averse while in other the exact opposite. Regret aversion, on the other hand, states that individuals wish to avoid losses for which they believe that they can have a better outcome ex post. In the housing market framework, sellers will want to avoid the regret of selling their house for less in a depressed market than later when the market turns, and they can sell for a higher price.

In this study, I incorporate the behavior aspects of the market participants together with the macroeconomic factors that affect the housing market. Behavior is defined as the impact of an action of a participant in the market, in this cases the transferees relocating and selling their home. Signal is defined as a measure related to the overall conditions of the market.

I posit that certain behavior aspects of sellers and buyers can serve as stress indicators. It has been shown that economic conditions can influence the time a buyer takes to evaluate and determine a property they would like to bid on, i.e. their search time.

Similarly, one’s past experience in the housing market shapes his or her decision making behavior (Baryla et al., 2000). Several studies have shown that loss aversion in sellers and the reference price effects impact the time the property is on market, i.e. prolongs the days on market (Anglin et al., 2003). The sellers are reluctant to decrease the listing price and only do so if no offers are coming their way (regret aversion). I stipulate that the bearish market expectations of the “informed sellers” will exacerbate their loss aversion and regret

170 aversion relative to their reference point because of the downward pressure of the housing prices. For example, if the “informed sellers” in this study expect market prices to decrease, they would decrease the time between when an offer is extended and when it is accepted.

If the sellers have bullish market expectations, they would like to hold exercising their option of accepting the offer until the last possible moment waiting for a better offer. Thus, by examining the behavior of the “informed sellers” relative to their time to accept an offer,

I can assess the market expectations and detect early signs of stress. Similarly, I can examine the combined effect of loss aversion behavior and market expectations of the

“informed seller”. In times of a depressed market, loss aversion will be much more prominent because the seller will be experiencing loss compared to the reference point of their original price.

Thus, if I examine the listing price compared to the selling price as well as the equity of the “informed seller”, I can deduce the market expectations of the participants- higher listing to selling price percentage will mean that a discount is not required to sell the property. Days on market variable not only indicates the health of the market but it can also be affected by the behavior of the “informed seller” as a result of price setting. As previously discussed, loss aversion and regret aversion affect the listing price the seller sets which has a direct effect on time it takes for the property to be sold. In a decreasing market, setting a price that is higher than the value determined by the market will lead to longer time the property will stay unsold. I now examine each of the indicators I have selected to include in the model and assign a category of signal, behavior or mixed. Table presents the preliminary set of variables, their category as signal, behavior or mixed, source of data

171 and brief explanation for including them in the model. I further examine each selection and the reasoning behind it in the following section.

Table VI List of Variables categorized as behavior, signal or mixed

Variables Indicator Expected Explanation/Expectation Source Type Sign Stress Index Dependent Transformed 10- and 20- City monthly and aggregate S&P500/Case Variable indices Shiller 20-city Monthly index LOWEQTY Mixed + This is a categorical variable . If equity is less than 5% of Private Database the value of the house, the value assigned is 1 indicating the transferee is “under water”, otherwise 0 FOACCEPT Behavior - Days between the date an offer is presented to the Private Database transferee from the relocating company on behalf of the employer to the date the employee accepts. Longer period of acceptances indicate that the transferee is bullish and expects a better offer from the market, while shorter period will expect bearish behavior where the transferee does not expect better offer from the market DOM Mixed _ Days on market, the house has been listed till contract is Private Database signed; higher DOM indicates depressed markets as well as loss aversion and regret aversion DSTOCL Mixed _ Days Sale to Close – how long it takes from contract to Private Database actual closing- shorter period shows bullish markets; longer periods show bearish markets AVLP Behavior + % of Acquisition price vs listed price – how accurate the Private Database listing was compared to the actual price being offered to the transferee either as average of two appraisals or based on an offer coming from a buyer- the lower the percentage could show some loss aversion behavior as well as discount to sell in a depressed market LVSP Behavior + % of Listed vs Sales Prices- it shows how well the house Private Database was priced when listed also it can show Loss Aversion where the seller (transferee) would like to list above the probable sales prices to avoid loss. Additionally, higher percentage of listing to selling AFFORD Signal ? Variable for affordability of the market- higher Monthly index affordability means healthier market (3month moving published by average) National Association of Realtors LENDSTND Signal Tightening standards by the lending officers can signal The quarterly stress. To avoid risk in defaulted mortgages, the bank will survey of impose higher standards for receiving credit (3 month Lending + moving average) Officers as proxy for tightening standards FLOW Signal Proxy for credit availability. Credit availability has mixed The quarterly effect. On one hand, credit availability increases seasonally affordability and fuels the economy. On the other hand, adjusted flow credit availability increases speculative behavior and leads growth rate to bubble creation (3 month moving average) related to ? household mortgages and liabilities ( Series FG153165105) DELQ Signal Delinquency rate in single residential mortgages. Higher Federal Reserve rates will signal stress in the market due to inability to pay of St. Louis- the current mortgage or refinance because of falling prices ( Delinquency + 3 month moving average) Rate On Single- Family Residential Mortgages,

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Booked In Domestic Offices, All Commercial Banks, Percent, Quarterly, Seasonally Adjusted

HOME_VOL Signal ? Home volume Seasonally adjusted monthly annual rate of single homes lost published by US Census Bureau UNEMPLOY Signal + Unemployment rate has direct impact on the affordability The monthly and ability of home owners to continue paying their statistics mortgages. published by National Bureau of Labor Statistics

Credit availability is an important factor in the housing market. Allen and Carletti

(2013) determine that higher credit availability leads to higher prices. Speculators find it very profitable to borrow from banks and enter the market. Banks on the other hand have informational asymmetry in regards to assessing the risk taken by the speculators.

Secondly, consumers who own their houses have knowledge of the real estate market because “they live in it” (Allen and Carletti, 2013, p. 30) while speculators are not familiar with the markets so they need to pay a participation costs which is dependent on the liquidity and interest rates- if they are below the participation cost level, then speculator will enter the market otherwise they will abstain. Their liability is also limited—no default penalty thus, they are willing to invest in riskier real estate and pay (Levitt and Syverson)

(Kurlat and Stroebel) higher prices than the fundamental value which subsequently creates a bubble. Goodhart and Hofmann (2008) determine that exogenous changes to credit supply have effect on house prices because “an increase in the credit supply lowers lending rates and stimulates current and future expected economic activity” (Goodhart and

Hofmann, 2008, pg.182). Additionally, credit availability can increase demand for the

173 borrowing-constrained households and since supply adjustments lag demand increases, the market will experience higher prices. That is why, I include the flow of funds related to household mortgages and liabilities in addition to lending standards to model the effect of credibility on the market. I expect that tightening lending standards signals stress, and I expect a mixed effect of the flow of funds. Generally, higher flow will lead to higher level of housing prices.

Affordability in the market also leads to higher prices and higher market participation. If house prices rise faster than income, then affordability may decrease. I use the National Association of Realtors Housing Affordability Index which is calculated on the relationship between median home prices, median family incomes, and the average effective mortgage interest rate. The higher the index, the stronger the household purchasing power is. Additionally, depressed markets could also dampen the affordability because of the impact on income and availability to get credit. I also include employment levels. Quigley (2002) shows that economic fundamentals such as population, aggregate employment, number of constructions permits are important in predicting housing prices.

I include the days on market (DOM), days from contract to close (DSTOCL) from the transactional private data of relocation home sales. I categorize those as mixed. From a behavioral perspective, the behavior of the seller and how they price set have a direct effect on the days on market while from an efficient market theory, days on market are affected by the supply and demand and overall health of the market. Days to close

(DSTOCL) represents how long it takes the buyer to finalize the transaction. A sale is contingent on receiving the mortgage loan. After offer is accepted and contract is executed, the buyer needs to go through the underwriting process to obtain funding. Holding all the

174 other factors that affect the time to obtain a loan, I posit that market conditions also affect that time to close. Closing the loan is contingent on appraisal of property, final income verifications etc. Thus lending standards, credit availability and individual financial situation of the buyer play a role. In times of increasing market, bull market, I expect the time to close the loan to be less while in decreasing markets, the time increases.

Additionally, the loan is contingent on the appraisal of the property, so behavior regarding determining market value plays a role. Black et al. (2003) show that the choice of comparable sales is affected by the sales price anchoring bias where appraisers use the most recent information to determine value (Gallimore, 1994) and are more likely to adjust a low valuation higher and accept a gain than adjust downward a valuation and accept a loss

(Havard, 1999).

I identify the time to accept an offer for a sale (FOACCEPT), the percentage of acquisition price vs listing price and the percentage of listing price vs. sales price to outside buyer as behaviors. 30 I posit that regret and loss aversion as well as market expectations shape the behavior of the “informed seller” when determining the listing price. The market and the buyer expectations of the economic conditions affect the final sales price. I believe that by including those behavior variables in the model I can capture the expectations of the participants in the market and detect stress signals earlier.

Additionally, I include the transformed lag values of the price index because of the strong auto correlation in housing prices (Case and Shiller, 1989). Similarly, Quigley

(2002) determine that one, two period lags explain 96% of the variation of the housing

30 Acquisition price is the price given by the relocation management company on behalf of the employer to satisfy the two-sale transaction process. More information related to the two-sale transaction process in available in Essay 1. Outside buyer is the bona fide third party buyer in the second transaction.

175 prices. More importantly though the first two monthly lags are “credible predictors of turning points” (Quigley, 2002, p.10).

4.4 Empirical Models

The base model and parsimonious model of the EWS are presented in equations (1) and (2). I expect to use our base model as a benchmark and improve on it by including the identified signals and behaviors. Further analysis related to the optimal lags of the variable will be performed in future research as part of the EWS design. The time series variables have been transformed by using 3 months moving average to reduce some of the white noise- affordability (AFFORD), unemployment (UNEMPLOY), flow of funds growth rate

(FLOW); delinquency rates(DELQ), lending standards (LENDSTD) and new home sale volume (HOME_VOL). The quarterly data has been transformed into monthly frequency by utilizing spline interpolation.

Base model

Stress_Indext= α +β1 Stress Index t-1 + β2Stress Indext-2 + ε (1)

Parsimonious model

Stress_Indext = α +β1 Stress Index t-1 + β2Stress Indext-2 + β3LOWEQTY + β4 FOACCEPT + β5 DOM + β6 DSTOCL + β7AVLP + β8 LVSP + β9 AFFORDt + β11 UNEMPLOYt + β12FLOWt + β13LENDSTNDt + β14DELQt + β15HOME_VOLt + ε (2)

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CHAPTER V

HYPOTHESES AND ECONOMETRIC FRAMEWORK

5.1 Hypotheses Formulation

The main hypothesis of this study is related to the explanatory power related to the variability in the housing market stress index. I posit that behaviors and signals explain the variability in the housing market stress index. Thus, I can create the foundation of the housing market early warning system. I also posit that there is a difference in the predictive power of the signals vs. behaviors. I expect signals to have stronger predictive power. I also posit that there is a difference in the predictive power of the model during different economic phases. In our study, I will examine the main hypothesis. The other three hypotheses will be addressed in our future research.

a. Main Hypothesis- H1: Behaviors and signals can explain variability in the housing market stress index b. H2: Behavioral patterns have stronger predictive power than signals c. H3: The predictive model is dependent on the use of specific stress index d. H4: There is difference in the predictive power of the model during different economic phases

The next section describes in detail our econometric approach

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5.2 Econometric Framework

5.2.1 Multicollinearity and autocorrelation In order to test our main hypothesis, I examine the data series for multicollinearity and stationarity of our variables. Many of the series have an underlying autoregressive nature. To avoid inflation of our t-statistics, I review the VIF factors of each one of the variables. Anything over 10 is considered to present higher multicollinearity so I consider dropping or replacing the variables. For future research, in order to test the predictive power of the model during different economic phases, I plan on splitting our data into the following sample periods – testing using the full period of 2003:Q1-2014:Q4, testing prior to the bubble burst 2003:Q1- 2006:Q4; 2007:Q1- 2009:Q4 and 2010:Q1- 2014:Q4. I use the commencement of the housing downturn as a reference point to split the data into sub- periods.

The autoregressive nature of our stress index and several of the other time series data dictates that I perform autoregression analysis. The procedure estimates and forecasts linear regressions when errors are heteroscadestic and autocorrelated. Autocorrelation causes inefficiency of the OLS model and biased standard-error estimates.

Heteroscadesticity, on the other hand, affects the variance of the errors and the accuracy of the forecast confidence intervals. In situations when both are present, the maximum likelihood method allows us to correct for both.

5.2.2 Optimal Lag Selection

When working within VAR framework, the issue of selecting optimal lag is crucial.

There are several methods of determining optimal lag selection when designing EWS models. Zigraiova and Jakubik (2014) determine optimal lag through univariate logit

178 model with their Financial stress index constructed into a binary variable and each of the indicators and their lags are tested. They further perform a Bayesian model averaging technique on the selected lagged variables as described in Babecky et al.(2012).Hacker and

Hatemi (2008) examine the information criteria used in the literature and their performance- Akaike information criterion(AIC), Schwartz Bayesian Criteria (SBC) and the Hannan-Quinn information criterion (HQC). The main goal is to identify a model that minimizes the information criteria. The authors examine the forecasting performance and lag-length distribution of that criterion in stable and unstable VAR models and when

ARCH is present or not. The results determine that SBC is the best performing in finding the appropriate lag when the “sample size and the largest coefficient in the coefficient matrix for the largest relevant lag are both sufficiently large” (Hacker and Hatemi, 2008, p.614). Additionally, with observations over 100, SBC is less sensitive to ARCH. The results are impressive when forecasting abilities are also tested.

Future research will evaluate the model performance using the different information criteria and will select the optimal lags using Schwartz Bayesian Criteria to select the model that minimizes the information criteria. Since I am focusing on short-term horizon, the model will focus on explaining stress with a short lag lead of six months.

Further research will expand the model focusing on explaining stress with long lag. The short-term models will include the autoregressive components.

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CHAPTER VI

PRELIMINARY RESULTS

6.1 Descriptive Statistics

Table displays the descriptive statistics of the variables in the model for aggregate stress indices31. I also examine for multicollinearity since many of the time series have high correlation. In addition, I run the descriptive statistics of the variables just for the crisis period- 2007:Q1- 2009:Q4 (Table VIII). When comparing the means of the variables, I can see the effect of the crisis on the variables. For example, LENDSTD shows increase which is consistent with the expectation that lending standards will be tougher. I see decline in the affordability and credit availability; FLOW shows 35 percent decline. On the other hand, unemployment increases. The effect of the crisis on the days on market (DOM) is very significant. I see a 16 day increase in the average. Similarly, I see that the listing price to sale price percentage decreases which is in line of our expectations. The time it takes a relocating employee to accept an offer increases slightly. Interestingly, the percentage of acquisition price to sales price increases to 100 percent. One of the explanations is that in time of significant losses, employers do over loss protection as shown in our essay related

31 Results related to the monthly indices are available upon request

180 to corporate transfers and housing market impact, thus I expect to see a lower listing price compared to acquisition price. The standard deviation of most of the variables also increases showing higher volatility in time of crisis. From a multicollinearity perspective, several variables show VIF higher than 10- FLOW and DELQ, which intuitively makes sense. Flow of funds will decrease because of higher risk and higher delinquency rates.

HOME_VOL and UNEMPLOY also show high VIF values. The decrease of volume of home purchases is affected by the decreased flow of funds, higher delinquency and higher unemployment.

Correlation coefficients for almost all variables are significant due to the large sample size.32 I perform correlation analysis for both 10 city and 20 city models. The only variables that do not show a significant relationship are the percentage of Listing vs. Sales prices (LVSP) and Unemployment.

Table VII Descriptive Statistics- 2003:Q1- 2014:Q4

Variable Label N Mean Std Dev Min Max VIF STRESS_20_A 52167 4.73 26.46 -37.14 37.31 STRESS_10_A 52167 5.60 31.61 -44.85 45.51 LENDSTND 52167 0.06 20.48 -24.10 75.50 2.85 FLOW 52167 6.37 5.99 -3.90 14.90 35.54 AFFORD 52167 139.43 31.14 102.10 207.47 9.92 LOWEQTY 52167 0.18 0.38 0 1.0000 1.09 DELQ 52167 4.73 3.75 1.38 11.26 70.79 HOME_VOL 52167 800.85 375.18 288.33 1316.33 39.31 UNEMPLOY 52167 6.18 1.74 4.40 10.00 15.07 FOACCEPT 52167 16.09 23.62 0 484.00 1.14 DOM 52167 76.80 79.87 0 1180.00 1.37 DSTOCL 52167 27.92 20.70 0 394.00 1.12 AVLP 52167 0.99 0.07 0.34 2.75 1.23 LVSP 52167 0.94 0.09 0 2.62 1.60

32 Due to the large size of the correlation matrix we do not show but it is available upon request

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Table VIII Descriptive Statistics – 2007:Q1- 2009:Q4- Crisis Period

Variable Label N Mean Std Dev Min Max STRESS_20_A 13118 1.05 19.47 -29.81 25.94 STRESS_10_A 13118 5.14 22.58 -29.87 34.24 LENDSTND 13118 26.39 24.73 1.90 75.50 FLOW 13118 4.14 3.52 -2.89 8.68 AFFORD 13118 130.58 20.39 108.43 175.00 LOWEQTY 13118 0.23 0.42 0 1.0000 DELQ 13118 4.29 2.51 2.00 10.81 HOME_VOL 13118 650.45 196.04 349.00 964.00 UNEMPLOY 13118 5.72 1.78 4.40 10.00 FOACCEPT 13118 17.87 25.60 0 484.00 DOM 13118 92.88 91.86 0 1132.00 DSTOCL 13118 24.20 20.58 0 394.00 AVLP 13118 1.00 0.09 0.42 2.51 LVSP 13118 0.91 0.09 0 2.08

6.2 Preliminary Results

The preliminary results from the autoregression analysis confirm the main hypothesis that signals and behaviors can explain variability of the housing stress market

(Table IX and Table X).

Since this is monthly data to account for seasonality I set the lags to be higher than 12, in this case I set the lags to be 15. The results show positive autocorrelation. Durbin- Watson statistic is 0.0082 and 0.0104 for the aggregate 10 and 20 city index respectively and 0.0272 and 0.0061 for the monthly 10 city and 20 city models which is below 2.00 indicating positive autocorrelation of the residuals. The ARCH/GARCH model displays DW statistics of 1.999 and 2.01 for the models effectively eliminating the autocorrelation. Table 9 and

Table 10 show the results of the ARCH/GARCH model for both models. The lag 1 and lag

2 of our stress index show opposite signs. This is in line with similar results recorded by

Quigley (1999). Oet et al. (2013) discuss that housing prices are lagged and would not have shown stress until 6 months later (July 2007); but if I model individual markets, stress indication may come earlier. All housing market variables are significant. From the signals

182 and behaviors- FOACCEPT and LOWEQTY are consistently insignificant in all four models. Several of the variables show surprisingly different sign than the expected – delinquency and lending standards. I examine a different period from 2003: Q1- 2006: Q4 to see how signs may have changed after the crisis. Table XI shows the results for the aggregate stress indices models. The results for the monthly models are similar. When I examine the signs related to the housing market variables, I see that the signs are in line with what the expectations were. Thus, I can posit that the crisis has an impact related to the signs of some of the variables that can be affected by the monetary policies.

The models show very high R2 which is consistent with previous studies which test housing price models including lagged dependent variables Case and Shiller (1990) and

Quigley(1999) regression models of housing prices on market fundamentals similar to this study and lagged dependent variables yield R2 of 99 percent. Quigley (1999) posits that most of the variations of housing prices can be explained by previous price movements.

He tests autoregressive models with lagged variables and concludes that one and two- period lag explains more than 96 percent of the variation” (Quigley, 1999, pg.7).

Additionally, Felix Schindler (2011) explores the predictability and persistence of the price movement of the S&P500/Case Shiller Price Indices. He not only tests the 20 city Index but also tests the CPI deflated 20 city Index and finds significant positive autocorrelations and persistence of very high lag order.

Future steps in the analysis include examining how the model behaves in the different periods as well as determining the optimal lag of the time series signals that I have included in the model. Additionally, I intend to perform analysis related to the predictability power of signals and behaviors. With that being said, the preliminary results

183 are quite promising. The results show that signals and behaviors have additional explanatory power that could be utilized to determine stress.

Table IX Results – Base and Parsimonious Models

The results of the autoregressive arch/garch model is shown below for both the parsimonious and the base model. The Dependent variable for Model 10 city is our standardized deflated and adjusted for GDP growth aggregate index deviation from its cumulative average while the 20 city model pertains to the constructed time series for the 20 city Index.

Variable Expecte Parsimonious Base Parsimonious Base Model d Sign Model Model 10 City Model 20 City 10 City 20 City Intercept -4.4503***(- 0.0659 -3.2491*** (- 0.8383 166.67) 201.17) STRESS_10_A_LAG + 0.9592***(3279.14 0.9713***(3545.56) 1.0560***(4195.54) 1 ) STRESS_10_A_LAG - -0.0275***(-91.42) -0.0330***(-115.63) -0.1138***(-442.17) 2 STRESS_20_A_LAG + 1.0526*** 1 ( 3577.09) STRESS_20_A_LAG - -0.1217***(- 2 423.81) AFFORD ? 0.0031***(84.12) 0.0002***(5.52) UNEMPLOY + 0.4142***(480.77) 0.3664***(538.17) HOME_VOL ? 0.0052***(697.81) 0.0043***(703.20) DELQ + -0.2949***(- -0.2478***(- 266.75) 271.70) LENDSTND + -0.0032***(-69.14) -0.0030***(-84.86) FLOW ? -0.1714***(- -0.1156***(- 602.30) 530.05) LOWEQTY + 0.002 (1.54) 0.0002 (1.34) FOACCEPT - -7.273E-6 (-1.59) -4.107E-6 (-1.08) DOM - -2.851E-6**(-3.44) -2.274E-6**(-3.46) DSTOCL - -2.446E-6 (-0.53) -8.833E-6 **(- 2.55) AVLP + 0.0495***(4.13) 0.0034**(3.22) LVSP + -0.0031**(-3.54) -0.0012*(-1.68) AR (1) -0.8953***(-80.64) -0.8293**(-11.43) AR(2) -0.1251***(-8.98) AR (15) 0.0120***(5.46) 0.0069**(2.03) GARCH(1) 4.976E-21*** 8.047E-21*** ARCH(0) ARCH(1) -2.24E-23*** 0.0004***(2766.03 -1.42E-23*** ) Regress R2 0.9998 0.9998 0.9998 0.9998

Number Obs 52167 52167 52617 52617 Note: t- statistic is shown in parentheses. Statistical significance at 10%, 5%, and 1% levels is indicated by * , **, and *** , respectively

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Table X Results – Base and Parsimonious Models Monthly Stress Index

The results of the autoregressive arch/garch model is shown below for both the parsimonious and the base model. The Dependent variable for Model 10 city is our standardized deflated and adjusted for GDP growth monthly change index as a deviation from its series average while the 20 city model pertains to the constructed time series for the 20 city Index.

Variable Expecte Parsimonious Base Parsimonious Base Model d Sign Model Model 10 City Model 20 City 10 City 20 City Intercept -6.6867***(- 0.8432 -3.3805*** (- 0.2529 366.45) 312.97) STRESS_10_M_LA + 0.0891***(322.37) 0.0973***(465.39) 0.1503***(565.53) G1 STRESS_10_M_LA - 0.0812***(- 0.0900***(-329.05) 0.1052***(381.33) G2 306.16) STRESS_20_M_LA + 0.1188*** G1 ( 425.81) STRESS_20_M_LA - 0.0611***(232.81) G2 AFFORD ? 0.0112***(264.58) 0.0024***(101.97) UNEMPLOY + 0.5613***(573.63) 0.2770***(537.71) HOME_VOL ? 0.0057***(589.66) 0.0029***(620.61) DELQ + -0.1214***(-70.62) -0.0286***(-33.63) LENDSTND + -0.0023***(-48.56) -0.0003***(-12.51) FLOW ? -0.2011***(- -0.0911***(- 633.66) 566.76) LOWEQTY + 0.0001 (0.47) 0.0001 (0.60) FOACCEPT - -4.221E-6(-0.72) -1.589E-6 (-0.54) DOM - -2.474E-6**(-2.75) -1.508E-6**(-3.01) DSTOCL - -3.948E-6 (-0.69) -1.897E-6 (-0.58) AVLP + 0.0057***(4.44) 0.0019**(2.59) LVSP + -0.0021**(-2.48) -0.0010**(-2.09) AR (1) -0.8756***(-69.11) -0.9999**(-2.09) -0.8463***(-33.62) -0.9957***(-26.25) AR(2) -0.1359***(-7.20) AR (15) GARCH(1) 2.83E-19*** -9E-21*** 0.9997*** ARCH(0) 0.0008***(2599.38 0.0002***(2578.43 2.2073E-8*** ) ) ARCH(1) 7.879E-23*** 0.0002***(575.92) Regress R2 0.9341 0.9153 0.9998 0.9998

Number Obs 52167 52167 52617 52617 Note: t- statistic is shown in parentheses. Statistical significance at 10%, 5%, and 1% levels is indicated by * , **, and *** , respectively

185

Table XI Results –Parsimonious Models Aggregate Stress Index : 2003: Q1- 2006: Q4

The results of the autoregressive arch/garch model is shown below for both the parsimonious and the base model for the period 2003-2006. The Dependent variable for Model 10 city is our standardized deflated and adjusted for GDP growth monthly change index as a deviation from its series average while the 20 city model pertains to the constructed time series for the 20 city Index.

Variable Expecte Parsimonious Parsimonious Model d Sign Model 20 City 10 City Intercept -15.8228***(- -10.3636*** (-615.50) 79.66) STRESS_10_A_LAG + 0.9763***(1959.44 1 ) STRESS_10_A_LAG - 0.0432***(70.39) 2 STRESS_20_A_LAG + 1.0831*** ( 2711.09) 1 STRESS_20_A_LAG - -0.0790***(232.81) 2 AFFORD ? 0.0737***(735.24) 0.0437***(586.36) UNEMPLOY + 0.1221***(73.24) 0.0927***(64.87) HOME_VOL ? 0.0035***(504.29) 0.0026***(573.90) DELQ + 1.7698***(191.17) 1.1264***(141.43) LENDSTND + 0.0186***(211.97) 0.0123***(207.57) FLOW ? -0.1263***(- -0.0769***(-425.41) 455.04) LOWEQTY + 0.0005 (0.88) 0.0004 (1.09) FOACCEPT - -7.663E-6(-1.06) -4.946E-6 (-1.10) DOM - -4.257E-6**(-2.35) -3.109E-6**(-2.49) DSTOCL - -2.032E-6 (-0.32) -6.995E-6 (-0.13) AVLP + 0.0026*(1.87) 0.0019*(1.75) LVSP + -0.0014(-0.54) -0.0006(-0.36) AR (1) -0.8695***(-39.06) -0.8767***(-35.61) AR(2) AR (15) GARCH(1) 7.537E-21*** -1.091E-18*** ARCH(0) 0.0003***(1239.07 0.0002***(1099.44) ) ARCH(1) Regress R2 0.9992 0.9990

Number Obs 23320 23320 Note: t- statistic is shown in parentheses. Statistical significance at 10%, 5%, and 1% levels is indicated by * , **, and *** , respectively

186

CHAPTER VII

CONCLUSION

This study aims to create an early warning system for the U.S. housing market.

While a significant body of literature exists around creating early warning systems and financial condition indices to predict bank and currency crises, to the best of my knowledge, this paper is the first to attempt to design a system specifically for the housing market that incorporates behaviors and signals. I categorize each of the indicators in three main categories: signal, behavior and mixed. I transform the S&P500/Case Shiller 20 city index and 10-city index by deflating and adjusting to GDP growth. The time series is constructed as the deviations of the transformed indices to the series average for the monthly frequency and cumulative average for the aggregate series. To address stationarity

I perform autoregressive (VAR) regression utilizing ARCH/GARCH method. The preliminary results confirm the main hypothesis that signals and behaviors can explain the variability in the housing stress index. I find that housing market variables and “informed seller” expectations of the market have statistically significant explanatory power. The next steps in my research are to examine if difference between signals and behaviors explanatory power exists and if the selection of housing stress index has an effect on the

187 model. Additionally, I would examine the predictability of the model related to different time periods. I also need to expand the findings of this study related to the housing market stress index and address the negative deviations. The ultimate goal is to construct an early warning system that could detect increase in stress in the housing market. Recent history has shown how important the health of the housing market is to the overall stability of the financial system. By providing a way to identify stress levels in the housing market earlier, policy makers will have additional time to mitigate a potential crisis earlier.

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