Sales Concessions in the US Housing Market

J Real Estate Finan Econ DOI 10.1007/s11146-016-9592-x

Darren K. Hayunga1

# Springer Science+Business Media New York 2016

Abstract This article examines the use of concessions in the US housing market, specifically payments for closing costs, home warranties, and structural repairs. This is the first study to examine the motivations and characteristics of homeowners that utilize concessions. It also examines the impact concessions have on transaction prices and marketing durations. While the literature has attempted to determine if concessions can reduce marketing durations or increase transaction prices, the evidence is tainted by endogeneity and sample issues. Additionally, we find that relative bargaining power between buyers and sellers has a fundamental effect on how concessions alter prices and marketing durations. This aspect has been considered only narrowly in the extant literature. Our results demonstrate that when sellers have bargaining power, transactions including concessions exhibit higher prices and shorter marketing durations. Conversely, when buyers have greater negotiation leverage, transactions including concessions experience lower prices and longer marketing periods.

Keywords Buyers’ incentives . Sellers’ concessions . Sellers’ motivations . Closing costs . Home warranties . Home repairs

JEL Classification D12 . R21 . R31

Introduction

In real property markets, owners use concessions to motivate potential tenants to rent and buyers to purchase their properties. This article examines the concessions used by homeowners to motivate potential purchasers, specifically payments for closing costs,

I thank anonymous referees, editor C.F. Sirmans, Kelley Pace, and Anurag Mehrotra for helpful comments as well as the National Association of Realtors® for the data.

* Darren K. Hayunga [email protected]

1 Department of Insurance, Legal Studies, and Real Estate, Terry College of Business, University of Georgia, Athens, GA 30602, USA Hayunga home warranties, and structural repairs. We first investigate the motivations and characteristics of homeowners who include concessions to determine if any of these factors increase or decrease the propensity to use incentives. This analysis has not been considered previously by the housing literature. We examine motivations such as sellers’ urgency levels and reasons for selling as well as characteristics such as a seller’s race, income, and age. We also investigate the impact concessions have on the transaction outcome of prices and time on the market (TOM). We question whether sellers are rewarded with shorter TOM as is commonly thought, and/or if they are able to capitalize concessions into transaction prices. The effect of concessions on transaction outcomes has been examined previously, but this study addresses a number of sample and econometric issues that cloud the extant literature and our understanding of the effect concessions have on prices and marketing durations. The first issue this study addresses is sample selection. Prior studies use MLS datasets, which primarily will capture concessions at listing and possibly during the marketing period. However, our discussions and experiences reveal that concessions are commonly introduced during negotiations between a specific prospective buyer and the seller. With the notable exception of Winkler and Gordon (2015), MLS datasets do not seem to record such late-stage concessions. For example, Soyeh et al. (2014)find only 3.3% of their sample include concessions. Also, Johnson et al. (2000) only analyze observations with transaction prices less than $100,000 because they do not find concessions used for higher-priced homes. We find that over 40% of the transactions in our sample include concessions and over 90% of those transactions have a transaction price over $100,000. The reason we are able to observe the greater use of concessions is that we use data from the National Association of Realtors® (NAR). These data are ideally suited to our research objectives in four ways. The first is the higher capture rate, which is due to the NAR surveys being sent directly to sellers after the closing. The second is that the NAR survey is specifically tailored to collect the economic and demographic information that is pertinent to the sale but is generally unobserved in MLS and public datasets. These include the aforementioned sellers’ motivational factors and demographic information, as well as structural quality, nominal losses and gains, selling to acquaintances, and marketing methods beyond MLS. These measures thus allow us to document that the propensity to use concessions decreases when properties are sold to acquaintances, with greater sellers’ age levels, and when the sales are due to job relocations. We also find the propensity to include concessions increases when sellers indicate that their properties are too expensive to keep, when they move greater distances between the sale and purchase properties, and with longer holding periods The third way that the NAR dataset is well-suited to our research objectives is that its many unique variables provide strong instruments to control for simultaneity. This is critical because test statistics demonstrate that TOM, prices, and concessions are endogenous. The simultaneous solutions to the optimization of price and TOM while including incentives has not been considered by the literature. Concessions clearly are pecuniary benefits that directly impact net transaction prices. TOM can also be affected when comparing the net transaction prices to the property’s service flow. For instance, adding an incentive without fully increasing the list price by the pecuniary benefit Sales Concessions in the US Housing Market produces a housing service flow that is undervalued and therefore should result in a shorter TOM, all else held equal. The fourth way that the NAR observations fit our research objectives is that they form a national dataset. This allows us to understand the use of concessions beyond a local market. Further, we are able to examine the effect of concessions in hot (cold) markets when sellers (buyers) have more bargaining power. Recent literature has considered such situations but in narrower contexts. Winkler and Gordon (2015) examine foreclosed properties in Huntsville, Alabama and Soyeh et al. (2014)analyze the recent market downturn in Boca Raton, Florida. In each case, the authors are looking for unique market behavior and the impact of concessions based on buyers having more bargaining power. We take a more general approach by using the Carrillo (2013) measure of relative bargaining power. Consistent with concessions being introduced during negotiations, we find that relative bargaining power is a key factor in understanding the effect of concessions. When analyzing the sample prior to considering negotiation power, we find concessions have no significant impact on prices or TOM. However, this is an artifact of two countervailing effects. In markets where sellers have greater bargaining power, the results demonstrate that homes with payments for closing costs and credits for repairs experience higher transaction prices and shorter TOM. In markets where buyers have greater negotiating power, homes that include home warranties and credits for repairs exhibit lower transaction prices. Transactions that include home warranties also experience longer TOM in buyers’ markets. We detail these results along with the rest of our analysis in the balance of this article. We begin in the next section with further background on the various ways that concessions can impact prices and TOM. We then present the extant literature, the data sample, and the empirical results in subsequent sections. The final section presents our conclusion.

Background and Motivation

Since they have not been considered previously, one catalyst for this study is to examine sellers’ motivations and other characteristics that increase or decrease the inclusion of concessions in transactions. After addressing the econometric issues, we are also prompted to conduct this analysis due to the myriad impacts concessions can have on prices and TOM. The theories showing how concessions should affect transaction outcomes are standard search and bargaining. In contrast, the empirical findings can vary based on: the relative relations between the net transaction prices and total value of the housing service flows, the point in the marketing process that the concessions are introduced, the concession type, as well as the preferences and constraints of certain groups of buyers and sellers. Consider first concessions added at the time the home is listed on the market so that they are an additional amenity of the respective property. The effect of the concessions will be partly a function of the relative relations between net transaction prices and housing service flows. In the simplest case, if sellers increase their list prices by the full value of the concessions, expected net transaction prices will be comparable with other properties having the same service flows. Therefore, expected TOM should not change, Hayunga the concessions will increase transaction prices given the higher list prices (conditional on the bargaining process), and so the econometrician should find a positive slope coefficient in price equations, ceteris paribus. If sellers do not increase their list prices or only partially relative to the concessions’ values, the properties are at least somewhat undervalued relative to other properties with the same service flows. Consequently, search theory shows that these properties should experience shorter TOM because the concessions increase the probability of sale (Wheaton 1990; Krainer 2001). Since the concessions are not fully priced, the slope coefficient on the incentive should be a reduced positive estimate or maybe even uncorrelated in the transaction prices model. Another situation that potentially undervalues certain properties can occur with the introduction of cash-constrained buyers. Assume again sellers increase their prices by the full amount of the concessions, but that the incentive in this case is payment of closing costs. All else held equal, a cash-constrained buyer will have a preference for the payment of closing costs over a substitute such as a list price reduction of the same amount. The net transaction prices will be equivalent, but a cash-constrained buyers will prefer the properties with the concession relative to others with the same service flow but without the added payment. The concession should be capitalized but, due to an increase in the probability of sale, search theory shows that TOM may also decrease conditional on the number of cash-constrained buyers in the market. At the other end of the price spectrum are possible situations when buyers do not fully value the concessions. Consider the case when a list price is too high due to necessary repairs, but a seller includes a credit that exactly offsets these costs. Conditional on the improvements meeting their tastes, buyers may prefer the repairs be completed by sellers prior to closing for two reasons. First, repairs completed by sellers are included in the total transaction prices and thus can be financed, which again helps cash-constrained buyers. Second, sellers may possess more information about costs relative to buyers. Sellers may have an information advantage in finding local contractors to perform the repairs, especially when purchasers move from other locations. The type of concession can also impact prices and TOM. For example, buyers may have a general preference for home warranties because sellers potentially know more about the quality of their properties and amenities. Buyers may thus prefer to reduce the costs of asymmetric information through the purchase of home warranties over list price reductions of the same amount. Payments for repairs offer an additional consideration from a seller’s standpoint. Owners may prefer paying buyers in cash for repairs of, for example, outdated carpet or appliances rather than replacing these themselves. Sellers will not know a specific prospective buyer’s penchant for quality and design so they prefer providing the payments at closing instead of guessing buyers’ tastes. A final factor to consider is the timing of concessions. When a concession is added at the time of listing or during the marketing period, the seller and prospective buyers can value it similarly to other transactional and structural features. However, what about the situations when buyers ask for concessions during the bargaining process? Our conversations with brokers and homeowners indicate that concessions are commonly included towards the end of the negotiations, and at two different points. The first is when a seller is attempting Sales Concessions in the US Housing Market to close negotiations with a specific buyer and the incentive is a final induce- ment to transact. The other is when a credit for repairs is prompted by the home inspection. To mitigate asymmetric information, buyers typically employ home inspectors and a new round of negotiation can occur based on the inspection report. Payments for discovered repairs may then become a new condition of sale after the initial price had been agreed upon. In either of these situations, it will be difficult for sellers to capitalize the concession values into prices at the end of negotiations, especially if a particular owner is anxious to sell and in the midst of bargaining with a specific interested buyer. Anxious sellers and concessions added during the negotiation phase introduce bargaining theory and participants’ relative leverage levels, which may also impact the effect concessions have on transaction outcomes. When sellers have bargaining power, concessions may be additional benefits that increase net transaction prices and/ or decrease TOM. Conversely, when they have bargaining power, buyers may seek economic rents in the form of concessions. Indeed, our findings support this reasoning regarding relative bargaining power.

Literature

Much of the concession literature in housing focuses on mortgage incentives.1 The literature examining non-mortgage concessions consists of Asabere and Huffman (1997), Johnson et al. (2000), Soyeh et al. (2014), and Winkler and Gordon (2015). The two early papers examine transaction prices but not TOM. None of the papers consider the simultaneous determination of prices, TOM, and concessions. Based on statistical tests, we find endogeneity is a fundamental consideration in modeling incentives and transaction outcomes. With the exception of Winkler and Gordon (2015), the literature also does not include TOM in price equations. We find this exclusion causes an omitted-variable bias. Our results demonstrate that TOM (prices) subsume the effect of concessions in price (TOM) equations. As mentioned in the introduction, the extant literature uses MLS datasets, which provides some understanding of the use of concessions at listing and during the marketing period. However, MLS datasets probably do not capture concessions introduced during negotiations unless brokers are diligent in enter- ing the information post-closing. Consequently, using keyword searches of the agent’s comment section within MLS, Soyeh et al. (2014)findonly3.3%of their sample includes concessions. Similarly, Johnson et al. (2000) analyze only observations of low cost homes because they do not find concessions used for transactions priced above $100,000. In contrast, Fig. 1 shows that the use of concessions spans the distribution of selling prices in the NAR data, albeit with a lower use of closing costs at the highest price points. The maximum house price in the first quantile in Fig. 1 is $111,000, and thus almost 90% of the

1 Mortgage concessions are primarily assumable loans, buydowns, and discount points. Literature examining these incentives includes Zerbst and Brueggeman (1977), Guntermann (1979), Colwell et al. (1979), Brueckner (1984), Sirmans et al. (1983), Smith and Sirmans (1984), and Ferreira and Sirmans (1989). Hayunga

60%* 50%

40%

All concessions 30% Home warranty 20% Closing costs Repairs 10%

Fig. 1 Percentage use of concessions within quantiles, with intra-quantile median transaction prices ($) on the y-axis total concessions are found in transactions with selling prices greater than $111,000. The exception to the underreporting based on using MLS datasets is Winkler and Gordon (2015). They use the Huntsville Alabama MLS and find that approximately 43% of foreclosed properties use concessions and almost 70% of non-foreclosed properties include concessions. In describing the data sample in the next section, we report that 44% of the NAR transactions include at least one concession.

Data Sample

We construct a sample from the NAR surveys for the US market from 2010 to 2012, which includes 2009 sales. We use more recent surveys because older questionnaires do not include critical information. For example, purchase price is one of the questions asked in the most recent surveys and is important since it is required to compute variables such as expected nominal gains and losses from sale and structure quality. By design, the surveys obtain the information important to the sale, including factors that are often not generally observed in housing data but that we find do affect transaction prices, TOM, and concessions. Table 1 details the many demographic and economic measures. Table 10 provides additional definitions of the variables. The properties are representative of US housing. The median residence sold is 25 years old with 3 bedrooms, 2 baths, and 2000 square feet. The median purchase price is $177,000 with a selling price of $220,000. In comparison, the US Census reports the median home value for the nation as $174,600 during 2010–2012. Regarding concessions, the survey responders indicate their use of closing costs, credit towards repairs, paying for a home warranty, and other non-realty concessions.2 Responders mark their use of concession but they do not reveal the dollar amount. We

2 The closing costs can include payment of condominium association fees but we restrict the sample to single- family residences and townhouses. Sales Concessions in the US Housing Market

Table 1 Descriptive statistics of data panel (N =3302)

Mean Median Std. Dev. Minimum Maximum

Purchase price ($) 222,232.86 177,000.00 190,321.12 10,400.00 3,600,000.00 List price ($) 300,181.28 235,000.00 269,205.36 9000.00 4,250,000.00 Sale price ($) 277,866.63 220,000.00 230,791.33 9000.00 3,590,000.00 Time on market (weeks) 19.31 10.00 27.26 0.00 208.00 Home age (years) 32.71 25.00 26.91 1.00 182.00 Square feet 2202.55 2000.00 988.80 703.00 9900.00 Number of bedrooms 3.39 3.00 0.82 1.00 9.00 Number of bathrooms 2.14 2.00 0.80 1.00 6.00 Holding period (years) 10.80 8.00 8.63 0.00 56.00 Purchase home warranty 0.25 0.00 0.43 0.00 1.00 Payment of closing costs 0.21 0.00 0.41 0.00 1.00 Credit for repairs 0.07 0.00 0.25 0.00 1.00 High urgency 0.17 0.00 0.38 0.00 1.00 Some urgency 0.44 0.00 0.50 0.00 1.00 No urgency 0.39 0.00 0.49 0.00 1.00 Reason for selling Avoid foreclosure 0.04 0.00 0.19 0.00 1.00 Relocation 0.19 0.00 0.39 0.00 1.00 Family change 0.08 0.00 0.27 0.00 1.00 Too expensive 0.03 0.00 0.16 0.00 1.00 First time seller 0.38 0.00 0.48 0.00 1.00 Short sale 0.03 0.00 0.17 0.00 1.00 Sold to a friend 0.07 0.00 0.25 0.00 1.00 Number of children 0.85 0.00 1.10 0.00 8.00 African American 0.02 0.00 0.13 0.00 1.00 Asian 0.03 0.00 0.16 0.00 1.00 Caucasian 0.93 1.00 0.25 0.00 1.00 Hispanic 0.02 0.00 0.15 0.00 1.00 Income to 25k 0.01 0.00 0.12 0.00 1.00 Income 25–35k 0.03 0.00 0.17 0.00 1.00 Income 35–45k 0.04 0.00 0.20 0.00 1.00 Income 45–55k 0.05 0.00 0.21 0.00 1.00 Income 55–65k 0.06 0.00 0.23 0.00 1.00 Income 65–75k 0.07 0.00 0.26 0.00 1.00 Income 75–85k 0.08 0.00 0.27 0.00 1.00 Income 85–100k 0.11 0.00 0.32 0.00 1.00 Income 100–125k 0.18 0.00 0.38 0.00 1.00 Income 125–150k 0.11 0.00 0.32 0.00 1.00 Income 150–175k 0.07 0.00 0.25 0.00 1.00 Income 175–200k 0.05 0.00 0.22 0.00 1.00 Income 200–250k 0.06 0.00 0.24 0.00 1.00 Income 250–500k 0.06 0.00 0.24 0.00 1.00 Income 500–1000k 0.01 0.00 0.11 0.00 1.00 Hayunga

Table 1 (continued)

Mean Median Std. Dev. Minimum Maximum

Income 1000k + ×10−1 0.01 0.00 0.50 0.00 1.00 Ages 20–29 0.04 0.20 0.00 0.00 1.00 Ages 30–34 0.11 0.32 0.00 0.00 1.00 Ages 35–40 0.13 0.33 0.00 0.00 1.00 Ages 40–44 0.11 0.32 0.00 0.00 1.00 Ages 45–50 0.11 0.31 0.00 0.00 1.00 Ages 50–54 0.11 0.31 0.00 0.00 1.00 Ages 55–59 0.11 0.31 0.00 0.00 1.00 Ages 60–64 0.11 0.31 0.00 0.00 1.00 Ages 65–69 0.09 0.29 0.00 0.00 1.00 Ages 70–74 0.04 0.20 0.00 0.00 1.00 Ages 75–79 0.02 0.14 0.00 0.00 1.00 Ages 80+ 0.01 0.11 0.00 0.00 1.00 Sellers’ ages (continuous variable) 49.92 49.00 13.69 22.00 89.00 MLS listing 0.80 1.00 0.40 0.00 1.00 Open house 0.55 1.00 0.50 0.00 1.00 Internet marketing 0.86 1.00 0.34 0.00 1.00 Magazine marketing 0.20 0.00 0.40 0.00 1.00 Print marketing 0.26 0.00 0.44 0.00 1.00 Sign in yard 0.75 1.00 0.44 0.00 1.00 Social media 0.06 0.00 0.24 0.00 1.00 Distance between sale and purchase properties 1 to 5 miles 0.24 0.00 0.43 0.00 1.00 6 to 10 miles 0.16 0.00 0.36 0.00 1.00 11 to 15 miles 0.09 0.00 0.28 0.00 1.00 16 to 20 miles 0.07 0.00 0.26 0.00 1.00 21 to 50 miles 0.09 0.00 0.28 0.00 1.00 51 to 100 miles 0.04 0.00 0.19 0.00 1.00 101 to 500 miles 0.10 0.00 0.30 0.00 1.00 501 to 1000 miles 0.09 0.00 0.29 0.00 1.00 More than 1000 0.13 0.00 0.33 0.00 1.00 Year 2009 0.04 0.00 0.19 0.00 1.00 Year 2010 0.23 0.00 0.42 0.00 1.00 Year 2011 0.45 0.00 0.50 0.00 1.00 Year 2012 0.28 0.00 0.45 0.00 1.00 Detached SFR 0.87 1.00 0.34 0.00 1.00 Townhouse 0.13 0.00 0.34 0.00 1.00 Suburb 0.08 0.00 0.27 0.00 1.00 City 0.10 0.00 0.30 0.00 1.00 Small town 0.37 0.00 0.48 0.00 1.00 Resort 0.01 0.00 0.10 0.00 1.00 Sales Concessions in the US Housing Market thus code the concession as a binary variable equaling one when used in the transaction and zero otherwise. We analyze the primary concession types of payments for home warranties, closing costs, and repairs. Of the 3328 total properties sold during the sample period, 819 sellers (24.6%) provide home warranties. The next most common concession is assistance with closing costs, which is included in 707 transactions (21.2%). Credit toward repairs is the other main concession occurring in 230 transactions (6.9%). The total number of transactions that use at least one incentive is 1476 (44.4%). Figure 2 details the annual use of concessions, which is generally stable across the years. The survey provides a number of categories that record various reasons owners are selling their properties. Nineteen percent indicate a job relocation, 8% note a change in the family status such as divorce or the birth of a child, and 3% indicate the property is too expensive to keep. Three percent of the sample indicate a desire to avoid foreclosure. Note that since the surveys are sent to individuals, these properties are not real- estate-owned properties. Regarding demographics, sellers’ incomes are generally distributed unimodally across the categories with the largest percentage having incomes between $100,000 and $125,000. Our analysis examines each of these categories coded as a binary variable and also a continuous measure of log income using the midpoint of each category with the top-coded upper category set to $1.5 M. When examining the sellers’ motivations to utilize concession, we find the continuous variable is more informative. In the analysis of the transaction outcomes, we use the binary variables. Sellers provide their ages and the proportion of responders at the various levels are consistent with the home-ownership lifecycle. The percentage of responders increases from 4% in ages 20–29 to 11% across most of the working years. The proportion begins to decrease at ages 65–69 and falls monotonically across the traditional retire- ment ages. Similar to sellers’ income levels, we compute a continuous variable for age

50%

40%

30% All concessions Home warranty Closing costs 20% Repairs

10%

0% 2009 2010 2011 2012 Fig. 2 Annual use of concessions Hayunga and find that it is more informative than the individual categories in the analysis of sellers’ motivations; otherwise, we use the binary variables. Other demographics indicate that first-time sellers are 38% of the sample, 18% of the sellers self-identify as highly urgent, and 43% are somewhat urgent. We note a majority of responders are Caucasian, which we use as the control group. While 93% of the responders identify themselves as Caucasian, this is less than the 95.8% in Harding et al. (2003a). Since the dataset provides the ZIP codes of the sold homes, we can control for property market conditions, labor markets, and other local considerations using fixed effects. We use up to 455 fixed effects at the 3-digit ZIP code level. The dataset also provides the ZIP code of the home purchased by the survey responder. We thus control for possible additional search and transaction costs using the separation distance between the sale and the purchase. Table 1 shows that 24% of the responders move 5 miles or less while 13% indicate a move of greater than 1000 miles. Using a continuous variable computed similar to the income and age measures, we find the median separation distance is 18 miles. There are a number of other transactional aspects that may correlate with prices, TOM, or concessions. These include the home age, location types such as city or small town, whether the home is a resort property, and numerous marketing methods in addition to MLS. We also create other possible determinants of prices, TOM, and the use of concessions. Since the dataset includes purchase prices, the first set are expected nominal losses and gains, structural quality, and holding period. Genesove and Mayer (2001) find that sellers set their list prices higher and experience longer TOM when they expect to suffer nominal losses upon sale. Bokhari and Geltner (2011) extend the model to incorporate expected gains. Losses and gains are the percent differences sellers will realize between the log purchase price and the expected log transaction price at the time of listing. The expected log transaction prices come from a hedonic price model using the sold properties in the sample. A positive value for expected losses indicates the percentage loss at the current average market price and is truncated from below at zero. Expected gain is the same measure but having a negative value and is and truncated above at zero.3 Structure quality is the residuals from a hedonic model at the time of purchase. The residuals are the portion of the previous sale prices that the regression did not predict. To the extent these qualities do not change significantly over time, the residuals are a noisy but reasonable measure of their impact on future transaction prices. We also compute and include measures for structural atypicality in all the models. If atypicality results in thin markets for instance, sellers may be motivated to decrease TOM through the use of concessions. Following Harding et al. (2003b), we compute binary variables representing the upper and lower

3 It may seem more logical to code a loss as negative and a gain as positive. However, we follow the literature in our coding, which provides the benefit of a more straightforward interpretation in the price and TOM models. That is, the literature demonstrates that sellers who expect a loss will set higher list prices. With loss as positive values, the slope coefficient in the price models is positive, which coincides with the increase in prices. If the loss is coded as a negative, the positive relation between loss and prices will produce a negative coefficient in a price model and may cause more confusion in explaining how the negative parameter estimate equatestoanincreaseinprices. Sales Concessions in the US Housing Market one percent of the distribution for multiple structural features. We define a new home as 2 years old or less while an old home is equal to or greater than 120 years old. A large home is greater than 5000 square feet and a small home is less than 900 square feet. A home has many bathrooms if there are 5 or more and many bedrooms if 6 or more. As another possible measure of atypicality, Harding et al. (2003b) include the inverse Mills ratio (IMR) using the Heckman two-stage correction model. The NAR data allow us to compute the IMR and potentially control for unique price-TOM preference for those sellers who may be fishing for high transaction prices and do not mind staying on the market longer than expected. Table 11 details the first stage model used to compute the IMR.

Motivations and Determinants of Concessions Use

Our first empirical analysis investigates for factors that prompt as well as dissuade homeowners to include concessions in their transactions. We regress the binary concession variables in probit specifications against the many economic, demographic, structural and transactional variables. Table 2 reports the findings. Column 1 reports the determinants when a transaction includes any one of the concessions and the other three specifications model the specific incentive named in the column heading. Somewhat unexpectedly, we note first that urgency does not increase the propensity of sellers to include concessions. It appears that an overall urgency effect is being subsumed by the reasons for sale and other transaction characteristics. For example, consistent with a preference for lower TOM, owners who indicate they are selling because the property is too expensive to keep exhibit an increase in the use of concessions, specifically home warranties. Another factor is the various marketing methods, which generally exhibit increases in the probability of using concessions. Positive parameter estimates suggest sellers using additional marketing methods such as open houses and Internetmarketingdosotoincreasebuyers’ interest levels, which may be a proxy for urgency. Similarly, concessions provide a feature not found in other properties with the same service flow and can increase buyers’ interest levels. The increase in the use of concessions with properties that are MLS listings is also consistent with brokers advising their clients to include concessions to increase the probability of sale; the sale being necessary for commissioned brokers to receive compensation. In addition, brokers may earn compensation from the companies that sell the home warranties. Owners selling due to job relocation significantly decreases the propensity to use concessions, specifically home warranties. This may be a function of a third party in the form of a corporate relocationfirminvolvedinthesetransactions. Another transaction that involves a third party is short sales, which consistently decrease the propensity to include concessions. This result is consistent with lending institutions being more apt to short properties in BAs is^ condition. In Table 2 and consistent throughout our analysis, we find that selling to a friend, acquaintance, or relative decreases the use of concessions. This is Hayunga

Table 2 Propensity to use concessions

All Closing Home concessions costs warranty Repairs

High urgency 0.006 −0.004 −0.010 0.013 (0.026) (0.021) (0.023) (0.014) Some urgency 0.001 0.008 −0.019 −0.002 (0.019) (0.016) (0.017) (0.010) Too expensive to keep 0.195*** 0.041 0.160*** 0.025 (0.053) (0.052) (0.048) (0.030) Job relocation −0.070** −0.038 −0.041* 0.023 (0.028) (0.023) (0.024) (0.015) Family change −0.032 −0.043 −0.035 0.013 (0.031) (0.028) (0.030) (0.016) Avoid foreclosure −0.086* −0.084** −0.089** −0.030 (0.046) (0.042) (0.043) (0.031) Sold to a friend −0.158*** −0.132*** −0.108*** −0.042* (0.038) (0.036) (0.036) (0.024) Log sellers’ incomes −0.032** −0.020 −0.016 −0.004 (0.015) (0.013) (0.013) (0.008) Log sellers’ ages −0.211*** −0.185*** −0.118*** 0.008 (0.050) (0.043) (0.045) (0.030) African American 0.113* 0.104** 0.006 0.025 (0.067) (0.048) (0.053) (0.031) Asian −0.016 0.013 −0.026 −0.005 (0.059) (0.051) (0.053) (0.035) Hispanic 0.040 −0.018 0.006 0.000 (0.057) (0.049) (0.047) (0.032) First time seller −0.027 0.014 −0.008 0.004 (0.021) (0.018) (0.019) (0.012) Expected loss 0.728*** 0.368*** 0.455*** 0.119*** (0.078) (0.068) (0.069) (0.038) Expected gain 0.642*** 0.306*** 0.484*** 0.125*** (0.062) (0.056) (0.055) (0.034) Short sale −0.239*** −0.122*** −0.193*** −0.045 (0.052) (0.047) (0.052) (0.031) Log number of earners −0.098** −0.014 −0.067* −0.043* (0.045) (0.038) (0.040) (0.024) Log number of children 0.001 −0.013 −0.000 −0.001 (0.018) (0.016) (0.017) (0.010) Log separation distance 0.015*** 0.007* 0.009** −0.001 (0.005) (0.004) (0.004) (0.003) Quality −0.737*** −0.395*** −0.497*** −0.103*** (0.064) (0.056) (0.056) (0.033) Holding period 0.030*** 0.016*** 0.021*** 0.005*** (0.003) (0.003) (0.003) (0.001) Log square feet −0.030 −0.089*** 0.048* 0.041** Sales Concessions in the US Housing Market

Table 2 (continued)

All Closing Home concessions costs warranty Repairs

(0.029) (0.024) (0.026) (0.016) Log home age 0.034*** 0.012 0.033*** 0.031*** (0.013) (0.011) (0.012) (0.007) MLS listing 0.094*** 0.071*** 0.076*** 0.013 (0.025) (0.021) (0.023) (0.014) Open house 0.054*** 0.020 0.040** 0.043*** (0.018) (0.016) (0.016) (0.011) Internet marketing 0.123*** 0.057** 0.107*** 0.028 (0.033) (0.029) (0.031) (0.020) Print marketing 0.013 −0.008 −0.011 0.010 (0.020) (0.017) (0.018) (0.011) Sign in yard 0.071*** 0.041** 0.086*** −0.000 (0.023) (0.020) (0.021) (0.013) Detached SFR −0.023 0.023 −0.019 −0.015 (0.028) (0.024) (0.025) (0.015) Suburban 0.021 0.012 0.025 0.041** (0.032) (0.027) (0.029) (0.016) City 0.008 0.023 −0.004 0.022 (0.030) (0.025) (0.027) (0.016) Small town 0.016 0.005 0.017 −0.002 (0.019) (0.016) (0.017) (0.011) Resort property 0.060 −0.123 0.021 0.100*** (0.086) (0.101) (0.081) (0.036) New home 0.092 0.100 −0.006 (0.127) (0.108) (0.132) Old home −0.150* −0.141* −0.163 −0.064 (0.090) (0.083) (0.107) (0.050) Small home −0.132* −0.117** −0.214*** 0.056 (0.071) (0.059) (0.077) (0.038) Large home 0.026 −0.108 −0.033 0.020 (0.070) (0.078) (0.057) (0.032) Many bathrooms 0.111 −0.029 0.037 −0.038 (0.092) (0.107) (0.080) (0.048) Many bedrooms −0.148 −0.014 −0.079 0.032 (0.101) (0.081) (0.080) (0.041) Observations 3261 3192 3125 3032 Pseudo-R2 0.130 0.115 0.139 0.108 Log-likelihood −1936.202 −1463.256 −1527.440 −716.830

Using probit models, the table presents the partial derivatives and heteroscedasticity-robust standard errors in parentheses for each incentive type. The first column equals one if any one of the concessions is included in the transaction and zero otherwise ** , ** and * denote p <0.01,p <0.05,andp < 0.10 respectively Hayunga notable because the apriorirelation is not entirely clear. Owners may offer better values to those they know by providing concessions but the result indicates the opposite. We find in analysis of the transaction outcomes in the next sections that selling to an acquaintance significantly and consistently decreases TOM but does not impact prices. The overall findings indicate that selling to acquaintances is done close to expected transaction prices so there is less motivation to include concessions as indicated in Table 2, and the transaction is conducted early in the marketing period. The results demonstrate that owner demographics also impact the propensity to include concessions. Greater sellers’ income and age levels decrease the probability of using concessions. These findings are consistent with lower preference for a quicker sale with higher incomes and being older. Race also impacts the use of concessions. Specifically, African American sellers provide payments for closing costs more often than the control group of non-Hispanic Caucasian sellers. Splitting the sample on whether owners are expecting a gain or a loss upon sale is a highly significant determinant of including concessions. The positive slope coefficients on expected losses indicates that, while sellers may set higher list prices to mitigate a loss, they have a higher likelihood to offer concessions. Thus, observed transaction prices may be greater when sellers are expecting a loss, as in Bokhari and Geltner (2011), but net transaction prices will be closer to the expected market value when concessions are considered. Expected gains is a negative loss so the positive parameter estimate in Table 2 denotes a lower probability to use concessions. Also highly predictive of the propensity to use concessions is structural quality, which exhibits an inverse relation. The finding is consistent with higher quality homes requiring fewer repairs; vice versa for lower quality properties. The negative marginal slopes across all incentive types may also indicate buyers’ preference for higher quality homes. The amount of time sellers have owned their properties affects the use of concessions. Across concessions types, longer holding periods increase the propensity to use concessions. The positive coefficients suggests greater deferred maintenance. Owners tend to improve their properties near the time of sale; thus, longer holding periods indicate greater deferred maintenance that increases the propensity to include concessions such as repairs and home warranties. This is similar to structural quality since deferred maintenance lowers quality and thus increases the probability of incentives in the transaction. Two structural attributes are significant predictors of concession use. An increase in the size of a home decreases the use of closing costs, but increases the use of home warranties and repairs; vice versa for smaller homes. The negative slope in the closing costs model is consistent with the decrease in Fig. 1 across home price segments. Additionally, the positive parameter estimates on home age are highly intuitive. All else held equal, older homes should require more repairs and have older appliances and equipment. Home age does not impact closing costs, but older homes exhibit an increase in the probability of including home warranties and repairs. Sales Concessions in the US Housing Market

Transactions Prices

We now consider the impact concessions have on transaction outcomes, beginning in this section with transaction prices. To do this, we must control for the simultaneity between prices, TOM, and concessions since the incentives we are examining are monetary benefits that influence net transaction prices. We use two stage least squares (2SLS) and instrumental variables (IV) in a system of equations. While finding high- quality instruments is often a challenge using MLS datasets, the NAR data offer strong instruments that correlate with the various incentive types but with neither TOM in price equations nor prices in TOM specifications. 4 Our empirical framework is accordingly a system of simultaneous equations, with the following specifications detailing the price system:

lnðÞ¼Pi j α þ TOMi þ Ci þ X i þ Z þ ω þ ϵi; lnðÞ¼TOMi j μ þ X i þ Z þ ω þ ξi; Ci ¼ jθ þ X i þ Z þ ω þ ηi;

Pi is the price of house i and TOMi is the marketing duration for the same property. Ci is equal to one if a concession is included in the transaction and zero otherwise. Xi is a vector of demographic and economic covariates. Z and ω are spatial and annual fixed effects that capture market conditions. ϵi, ξi and ηi are time-variant error terms that are assumed to be randomly distributed. When we include only the concession and initially withhold TOM (prices) as an independent variable in the price (TOM) models, we are able to compute the test statistics of endogeneity and report them at the bottom of each column. Because our models estimate a heteroscedasticity-robust variance-covariance matrix, we first report Wooldridge’s(1995) score test of exogeneity that indicates whether endogeneity exists between the dependent and independent variables. A second test we include detects weak instruments. We report both Shea’s partial R2 and the F statistic. The partial R2 measures the correlation between the IV and the instruments after partialling out the effect of the exogenous variables. Stock et al. (2002) note that the F statistic is often statistically significant even with weak instruments. They argue for the F statistic being greater than a threshold, generally set at approximately 10. To check the correlation between the instruments and the structural error term, a third test statistic we report is the Wooldridge (1995) robust score of over- identifying restrictions. Again, the Wooldridge score considers the robust variance-covariance matrix.

All Concessions

To confirm the endogeneity between marketing duration and prices, the specifi- cationincolumn1inTable3 includes the predicted value of TOM but initially

4 The concession IVs use linear probability models to avoid the forbidden regression and the incidental parameters problems. Appendix 3 further explains these issues and specifies the models. Hayunga

Table 3 Log prices using 2SLS

(1) includes TOM IV Standard error (2) includes Standard error incentive IV

Log TOM IV 0.054*** (0.015) All concessions IV 0.142*** (0.049) High urgency −0.081*** (0.017) −0.084*** (0.017) Some urgency −0.069*** (0.013) −0.065*** (0.013) Too expensive to keep 0.086** (0.037) 0.053 (0.038) Job relocation −0.063*** (0.019) −0.052*** (0.019) Family change 0.032 (0.021) 0.041* (0.021) Avoid foreclosure −0.001 (0.032) 0.002 (0.032) Income 35–44k 0.076 (0.048) 0.075 (0.048) Income 45–54k 0.046 (0.043) 0.050 (0.043) Income 55–64k 0.108*** (0.041) 0.085** (0.041) Income 65–74k 0.186*** (0.041) 0.176*** (0.041) Income 75–84k 0.201*** (0.038) 0.186*** (0.038) Income 85–99k 0.217*** (0.041) 0.202*** (0.042) Income 100–124k 0.272*** (0.039) 0.255*** (0.039) Income 125–149k 0.316*** (0.041) 0.301*** (0.041) Income 150–174k 0.352*** (0.043) 0.339*** (0.043) Income 175–199k 0.378*** (0.044) 0.360*** (0.043) Income 200–249k 0.424*** (0.044) 0.397*** (0.045) Income 250–499k 0.516*** (0.043) 0.499*** (0.043) Income 500–999k 0.579*** (0.057) 0.581*** (0.056) Income 1,000k + 0.700*** (0.085) 0.639*** (0.085) Ages 30–34 0.005 (0.027) 0.021 (0.028) Ages 35–30 0.008 (0.029) 0.041 (0.029) Ages 40–44 0.082*** (0.030) 0.116*** (0.029) Ages 45–50 0.088*** (0.032) 0.121*** (0.030) Ages 50–54 0.093*** (0.035) 0.135*** (0.032) Ages 55–59 0.064* (0.037) 0.118*** (0.033) Ages 60–64 0.114*** (0.037) 0.172*** (0.033) Ages 65–69 0.159*** (0.039) 0.216*** (0.036) Ages 70–74 0.097** (0.049) 0.160*** (0.047) Ages 75–79 0.145** (0.058) 0.201*** (0.055) Ages 80+ 0.101 (0.069) 0.182*** (0.069) African American −0.269*** (0.054) −0.269*** (0.056) Asian 0.103*** (0.036) 0.108*** (0.037) Hispanic −0.017 (0.041) −0.024 (0.040) Expected loss 0.198** (0.080) 0.034 (0.077) Expected gain −0.167** (0.070) −0.314*** (0.067) Short sale −0.217*** (0.038) −0.163*** (0.037) Log number of earners −0.036 (0.034) 0.008 (0.034) Log number of children −0.017 (0.013) −0.015 (0.013) 1–5 miles S.D. −0.062*** (0.023) −0.044* (0.023) 6–10 miles S.D. −0.099*** (0.025) −0.086*** (0.025) 11–15 miles S.D. −0.088*** (0.027) −0.074*** (0.026) 16–20 miles S.D. −0.125*** (0.027) −0.111*** (0.028) 21–50 miles S.D. −0.098*** (0.026) −0.084*** (0.026) Sales Concessions in the US Housing Market

Table 3 (continued)

(1) includes TOM IV Standard error (2) includes Standard error incentive IV

51–100 miles S.D. −0.079** (0.032) −0.066** (0.032) 101–500 miles S.D. −0.055** (0.023) −0.053** (0.023) 501–1000 miles S.D. 0.008 (0.025) 0.019 (0.025) Quality 0.751*** (0.071) 0.914*** (0.067) Holding period −0.007** (0.003) −0.015*** (0.003) Log square feet 0.736*** (0.019) 0.755*** (0.019) Suburban 0.043** (0.021) 0.036* (0.021) City 0.030 (0.020) 0.020 (0.020) Small town 0.046*** (0.012) 0.036*** (0.012) Resort property 0.275*** (0.068) 0.244*** (0.066) New home 0.139** (0.064) 0.120** (0.060) Old home −0.162*** (0.050) −0.143*** (0.048) Small home 0.064 (0.066) 0.074 (0.065) Large home −0.066 (0.060) −0.051 (0.060) Many bathrooms 0.273*** (0.060) 0.282*** (0.060) Many bedrooms −0.086 (0.066) −0.086 (0.068) Log home age −0.002 (0.009) First time seller −0.000 (0.015) Detached SFR 0.002 (0.018) Inverse Mills ratio 0.100 (0.275) Constant 6.610*** (0.188) 6.550*** (0.184) Observations 3263 3263 Adjusted R2 0.791 0.789 Robust score χ2 (p-value) 0.000 0.000 Partial R2 0.151 0.089 Robust F 52.26 27.93 Over-identification (p-value) 0.689 0.255

The models present determinants of housing prices. Model (1) includes the predicted value of TOM while Model (2) includes the predicted use of concessions. The models include fixed effects at the 3-digit ZIP code level as well as the year of listing. Heteroscedasticity-consistent and IV-robust standard errors in parentheses *** , ** and * denote p <0.01,p < 0.05, and p < 0.10 respectively

withholds the use of concessions. The first stage equation is provided in Ta- bles 13 and 14. The endogeneity statistics at the bottom of Model 1 demonstrate that TOM is quite endogenous with prices and the IV is neither weak nor over- identified. The TOM coefficient is positive and significant, which is consistent with search theory and the understanding that higher (lower) priced homes will tend to be on the market longer (shorter). Model 2 replaces TOM with the concessions IV. The reduced form equation is again detailed in Tables 13 and 14. The test statistics at the bottom of the Model 2 indicate that concessions are endogenous with prices, and the IV is neither weak nor over-identified. The parameter estimate on the predicted use of concessions is positive indicating an increase in prices when any incentive type is included in the transaction. Hayunga

Since TOM and concessions demonstrate endogeneity with transaction prices, we next model the full system of equations and report the three specifications in Table 4. The instruments are the same as those in Table 3. Column 1 details the price model. We observe that the inclusion of TOM subsumes the impact of concessions. TOM maintains a relation similar to Model 1 in Table 3 but the concessions measure is insignificant. The other independent variables in Model 1 meet with many of our expectations and offer a number of noteworthy fundamental findings that have not been reported previously in the literature. The first is that higher sellers’ urgency levels decrease prices. Using Kennedy (1981) for proper interpretation of a binary variable in a logarithmic equation, the coefficient on high urgency equates to a discount of 7.57% compared to non-urgent owners, while somewhat urgent sellers realize price reductions of 6.34%. Sellers expressing that the home is too expensive to keep experience an increase in transaction prices. At first glance this may seem counterintuitive if these sellers are cash constrained but concessions help to explain this finding. These owners have a greater probability of including concessions and to let their properties stay on the market longer. Thus, the positive coefficient in the price model is consistent with search theory: being on the market longer and introducing incentives results in higher transaction prices. Many seller demographics correlate with prices. The slope coefficients increase monotonically across the income levels, which is consistent with sellers’ who earn higher incomes also own more expensive homes. Age also increases prices, which can be a function of older individuals owning more expensive homes as well as older sellers having a lower preference for liquidity. Addi- tionally, race impacts prices. African Americans experience a decrease in values while Asian sellers realize higher prices than non-Hispanic Caucasian sellers. Sellers who expect to experience losses upon sale realize higher transaction prices. This is consistent with the setting of higher list prices found by Genesove and Mayer (2001) and Hayunga and Pace (2016). We note that these sellers also have a significantly greater propensity to use concessions in column 2 and stay on the market longer as indicated in column 3. Again, these results are consistent with search theory. There are additional structural and transaction characteristics that correlate with prices. Similar to the findings by Aroul and Hansz (2014), short sales realize a loss of approximately 20%. Throughout our analysis and in Table 4, short sales also exhibit longer TOM and a negative propensity to use concessions. As can be expected, home quality levels are a strong determinant of prices. Higher (lower) quality homes obtain higher (lower) prices. Longer holding periods indicate a slight decrease in transaction prices. This is consistent with the previous discussion indicating that longer holding periods may indicate greater deferred maintenance.

Individual Concessions

Each concession type can be used by sellers differently and thus may have unique economic characteristics that are not captured using an all-inclusive Sales Concessions in the US Housing Market

Table 4 Log prices using a system of simultaneous equations

Log price All concessions Log TOM

All concessions IV 0.036 (0.065) Log TOM IV 0.045** (0.022) High urgency −0.082*** −0.003 −0.036 (0.018) (0.027) (0.060) Some urgency −0.068*** −0.015 0.040 (0.013) (0.020) (0.044) Too expensive to keep 0.079** 0.379*** 0.299** (0.040) (0.058) (0.127) Job relocation −0.060*** −0.086*** −0.047 (0.021) (0.031) (0.068) Family change 0.035 0.002 0.112 (0.022) (0.033) (0.073) Avoid foreclosure 0.001 −0.039 −0.079 (0.030) (0.046) (0.100) Income 35–44k 0.076* 0.004 0.001 (0.040) (0.062) (0.135) Income 45–54k 0.046 0.010 0.128 (0.038) (0.059) (0.129) Income 55–64k 0.103*** 0.143** −0.061 (0.038) (0.057) (0.124) Income 65–74k 0.183*** 0.037 −0.046 (0.035) (0.055) (0.119) Income 75–84k 0.198*** 0.026 −0.191 (0.035) (0.054) (0.118) Income 85–99k 0.214*** 0.052 −0.111 (0.034) (0.052) (0.115) Income 100–124k 0.269*** 0.053 −0.169 (0.033) (0.050) (0.110) Income 125–149k 0.313*** 0.006 −0.259** (0.035) (0.053) (0.116) Income 150–174k 0.350*** −0.037 −0.320** (0.037) (0.057) (0.125) Income 175–199k 0.374*** −0.001 −0.276** (0.040) (0.061) (0.133) Income 200–249k 0.419*** 0.030 −0.397*** (0.040) (0.059) (0.130) Income 250–499k 0.513*** −0.008 −0.328** (0.039) (0.059) (0.130) Income 500–999k 0.579*** −0.081 −0.109 Hayunga

Table 4 (continued)

Log price All concessions Log TOM

(0.060) (0.093) (0.203) Income 1000k + 0.687*** 0.254 −0.739* (0.127) (0.194) (0.423) Ages 30–34 0.008 −0.119** 0.044 (0.033) (0.050) (0.109) Ages 35–30 0.015 −0.204*** 0.141 (0.035) (0.051) (0.112) Ages 40–44 0.089** −0.231*** 0.080 (0.036) (0.052) (0.115) Ages 45–50 0.095*** −0.266*** −0.041 (0.036) (0.054) (0.118) Ages 50–54 0.101*** −0.303*** 0.104 (0.038) (0.056) (0.121) Ages 55–59 0.075* −0.372*** 0.135 (0.041) (0.058) (0.126) Ages 60–64 0.126*** −0.372*** 0.177 (0.043) (0.060) (0.130) Ages 65–69 0.171*** −0.433*** 0.033 (0.045) (0.063) (0.138) Ages 70–74 0.110** −0.423*** 0.125 (0.050) (0.072) (0.157) Ages 75–79 0.157*** −0.336*** 0.225 (0.056) (0.083) (0.181) Ages 80+ 0.117* −0.487*** 0.363* (0.068) (0.095) (0.207) African American −0.270*** 0.140** 0.439*** (0.045) (0.070) (0.153) Asian 0.105*** −0.006 0.014 (0.037) (0.058) (0.126) Hispanic −0.018 0.046 −0.011 (0.038) (0.059) (0.130) Expected loss 0.161* 1.524*** 0.671*** (0.091) (0.094) (0.205) Expected gain −0.199*** 1.316*** 0.422*** (0.077) (0.073) (0.160) Short sale −0.205*** −0.286*** 0.312*** (0.041) (0.053) (0.116) Log number of earners −0.027 −0.336*** −0.015 (0.037) (0.050) (0.108) Log number of children −0.017 0.020 0.081* (0.014) (0.022) (0.048) 1–5 miles S.D. −0.058** −0.156*** −0.027 Sales Concessions in the US Housing Market

Table 4 (continued)

Log price All concessions Log TOM

(0.024) (0.035) (0.076) 6–10 miles S.D. −0.096*** −0.092** 0.035 (0.025) (0.037) (0.081) 11–15 miles S.D. −0.085*** −0.080* 0.091 (0.028) (0.042) (0.091) 16–20 miles S.D. −0.122*** −0.082* 0.092 (0.029) (0.044) (0.097) 21–50 miles S.D. −0.095*** −0.031 0.181** (0.027) (0.041) (0.090) 51–100 miles S.D. −0.076** −0.038 0.170 (0.035) (0.053) (0.116) 101–500 miles S.D. −0.054** −0.014 0.001 (0.024) (0.038) (0.082) 501–1000 miles S.D. 0.011 −0.049 0.069 (0.025) (0.038) (0.084) Quality 0.787*** −1.439*** −0.455*** (0.083) (0.077) (0.167) Holding period −0.009** 0.063*** 0.012 (0.004) (0.004) (0.008) Log square feet 0.741*** −0.003 0.119* (0.019) (0.030) (0.066) Suburban 0.042* 0.069** 0.012 (0.022) (0.034) (0.075) City 0.027 0.055* 0.007 (0.020) (0.031) (0.068) Small town 0.045*** 0.023 −0.148*** (0.014) (0.020) (0.044) Resort property 0.268*** 0.276*** 0.043 (0.059) (0.089) (0.194) New home 0.140** 0.263** −0.247 (0.070) (0.112) (0.245) Old home −0.160*** −0.193** 0.236 (0.057) (0.089) (0.194) Small home 0.066 −0.156** −0.205 (0.049) (0.075) (0.163) Large home −0.064 −0.055 0.165 (0.048) (0.074) (0.161) Many bathrooms 0.274*** 0.112 0.425** (0.059) (0.091) (0.199) Many bedrooms −0.084 −0.109 −0.316 (0.060) (0.093) (0.203) Log home age 0.076*** −0.023 Hayunga

Table 4 (continued)

Log price All concessions Log TOM

(0.014) (0.030) Detached SFR −0.042 0.285*** (0.030) (0.066) First time seller −0.076*** −0.080 (0.023) (0.050) Sold to a friend −0.110*** −0.372*** (0.037) (0.081) MLS listing 0.150*** 0.199*** (0.026) (0.057) Open house 0.121*** 0.411*** (0.020) (0.044) Internet marketing 0.135*** 0.298*** (0.034) (0.074) Magazine marketing 0.015 0.376*** (0.023) (0.050) Print marketing 0.009 0.170*** (0.022) (0.047) Sign in yard 0.062** 0.135** (0.024) (0.053) Inverse Mills ratio 0.732 3.165*** (0.506) (1.105) Constant 6.577*** 0.415 2.080*** (0.203) (0.332) (0.725) Observations 3263 3263 3263 Adjusted R2 0.825 0.317 0.474

The table reports a system of equations that include fixed effects at the 3-digit ZIP code level and the year of listing. Additional definitions of each variable are in Table 10. Heteroscedasticity-consistent and IV-robust standard errors in parentheses *** , ** and * denote p <0.01,p < 0.05, and p < 0.10 respectively

variable. We therefore model each concession individually and report the findings in Table 5. For each incentive type, we initially withhold the endogenous TOM variable to model the concession IV and confirm endogeneity test statistics.WethenaddtheTOMIVinafull system of simultaneous equations as in Table 4. Table 5 results across the various concession types can be succinctly summarized. As with the all-inclusive variable, when TOM is omitted from the structural equation, each concession is positively correlated with transaction prices. In addition, each concession IV is endogenous with prices, not weak, and not over-identified. However, in a system of equations that include marketing durations, the impact of concessions is Sales Concessions in the US Housing Market

Table 5 Log prices including specific concession types

Withhold log TOM System of equations

Panel A Closing costs IV 0.154** 0.055 (0.075) (0.082) Log TOM IV 0.035* (0.019) Constant 6.613*** 6.623*** (0.186) (0.196) Observations 3263 3263 Adjusted R2 0.804 0.827 Robust score χ2 (p-value) 0.014 Partial R2 0.047 Robust F 11.58 Over-identification (p-value) 0.453 Panel B Home warranty IV 0.166** 0.048 (0.072) (0.082) Log TOM IV 0.044** (0.019) Constant 6.709*** 6.617*** (0.169) (0.196) Observations 3263 3263 Adjusted R2 0.791 0.825 Robust score χ2 (p-value) 0.008 Partial R2 0.050 Robust F 18.78 Over-identification (p-value) 0.545 Panel C Repairs IV 0.374** 0.270 (0.176) (0.187) Log TOM IV 0.038** (0.017) Constant 6.672*** 6.619*** (0.193) (0.210) Observations 3263 3263 Adjusted R2 0.782 0.818 Robust score χ2 (p-value) 0.021 Partial R2 0.018 Robust F 9.37 Over-identification (p-value) 0.134

The table reports key parameter estimates of log housing price models. The model includes fixed effects at the 3-digit ZIP code and the year of listing. Heteroscedasticity-consistent and IV-robust standard errors in parentheses *** , ** and * denote p <0.01,p < 0.05, and p < 0.10 respectively Hayunga subsumed by TOM. Additionally, the positive parameter estimate on TOM is consistent with search theory.

Bargaining Power

The literature on non-mortgage concessions examines two situations when one can expect buyers to have greater bargaining power relative to sellers. Soyeh et al. (2014) examine concessions during the real estate downturn from 2009 to 2011. Winkler and Gordon (2015) investigate foreclosed versus non-foreclosed properties. We extend this idea in a more general way utilizing the approach from Carrillo (2013). He creates a measure of relative bargaining power, denoted as θ, with high (low) values indicating a hotter (colder) market and sellers having greater (lower) bargaining power. This measure allows us to reclassify the entire US housing market into submarkets based on negotiating leverage. We then split the sample into quartiles based on the value of θ and model each concession using transactions in the upper and the lower quartiles. Since θ is computed for each observation at a submarket level, we remove the spatial fixed effects from the models as they are collinear. Table 6 reports the findings. The results indicate that the previously reported insignificant relation between prices and concessions can better be understood as a commingling of two opposing effects. Panel A presents the transactions when sellers have greater bargaining power. The results demonstrate that when concessions are part of the sale in hot markets, incentives—specifically payments for closing costs and repairs—increase transaction prices. This finding suggests that, when sellers have more bargaining power and still include concessions, it is an added benefit that is capitalized into transaction prices. Panel B reports the transactions with lower θ. We observe the opposite effect. Transactions with payments for closing costs and home warranties experience a decrease in transaction prices. The results are not implying an improper causation that posits adding a concession reduces value. Instead, the finding is consistent with several rationales that are not necessarily mutually exclusive. The result is consistent with concessions added towards the end of negotiations and sellers not wanting to lose interested buyers in cold markets. They thus do not or do not feel able to capitalize the incentives into the prices. Similarly, buyers may be extracting rents in the form of reduced transaction prices along with concessions. These properties may also have been on the market longer than expected so owners add incentives to increase the arrival rate of buyers. To consider this last aspect further, we next analyze the effect of concessions on TOM, including in cold markets.

Marketing Durations

To examine the impact of concession on TOM, we begin with a restricted model that initially withholds the transaction prices as an independent variable to allow for Sales Concessions in the US Housing Market

Table 6 Log transaction prices split on relative bargaining power

All concessions Closing costs Home warranty Repairs

Panel A: Higher θ All concessions IV 0.526*** (0.159) Closing costs IV 0.489* (0.282) Warranty IV 0.136 (0.146) Repairs IV 0.909* (0.477) Constant 5.613*** 5.170*** 5.833*** 6.184*** (0.712) (0.788) (0.641) (0.723) Observations 829 829 829 829 Adjusted R2 0.508 0.496 0.613 0.468 Panel B: Lower θ All concessions IV −0.584*** (0.180) Closing costs IV −0.498** (0.206) Home warranty IV −0.637** (0.279) Repairs IV 0.092 (0.349) Constant 6.674*** 6.539*** 5.772*** 6.389*** (0.605) (0.517) (0.590) (0.471) Observations 792 792 792 792 Adjusted R2 0.538 0.648 0.545 0.705

The table reports the impact of concessions on log transaction prices after splitting the sample based on the Carrillo (2013) index of relative bargaining power (θ). Panel A presents the transactions in the quartile with sellers having greater bargaining power. Panel B reports results using the transactions in the quartile with buyers having greater bargaining power. All specifications are part of a full system of equations that controls for the endogeneity of both concessions and TOM. Heteroscedasticity-consistent and IV-robust standard errors in parentheses *** , ** and * denote p <0.01,p < 0.05, and p < 0.10 respectively confirmation of endogeneity tests. Column 1 in Table 7 presents this specification. The results demonstrate that transactions that include concessions experience an increase in TOM. Note though that the test statistic at the bottom of the column indicates that the use of concessions is not endogenous with TOM. This is not caused by a poor IV as the other tests indicate a strong IV that is not over-identified. Hayunga

Table 7 Structural equations of log TOM

(1) includes concessions IV (2) uses non-IV concessions

All concessions 0.183* 0.226*** (0.109) (0.040) Log selling price IV 0.162** (0.077) Too expensive to keep 0.232* 0.227* (0.120) (0.125) Job relocation −0.078 −0.040 (0.052) (0.062) Family change 0.101 0.100 (0.066) (0.066) Avoid foreclosure −0.096 −0.084 (0.088) (0.089) Sold to a friend −0.356*** −0.347*** (0.085) (0.083) Income 35–44k −0.014 −0.009 (0.132) (0.133) Income 45–54k 0.095 0.095 (0.123) (0.126) Income 55–64k −0.125 −0.127 (0.119) (0.120) Income 65–74k −0.102 −0.114 (0.117) (0.121) Income 75–84k −0.220* −0.246** (0.115) (0.120) Income 85–99k −0.150 −0.174 (0.112) (0.118) Income 100–124k −0.220** −0.245** (0.106) (0.113) Income 125–149k −0.287*** −0.321*** (0.110) (0.118) Income 150–174k −0.347*** −0.385*** (0.114) (0.126) Income 175–199k −0.325*** −0.354*** (0.125) (0.137) Income 200–249k −0.437*** −0.478*** (0.123) (0.136) Income 250–499k −0.363*** −0.414*** (0.122) (0.139) Income 500–999k −0.178 −0.226 (0.179) (0.196) Income 1000k + −0.848** −0.941** (0.417) (0.432) African American 0.410*** 0.435*** (0.137) (0.140) Asian 0.031 0.015 Sales Concessions in the US Housing Market

Table 7 (continued)

(1) includes concessions IV (2) uses non-IV concessions

(0.121) (0.122) Hispanic −0.024 −0.032 (0.108) (0.108) First time seller −0.093** −0.092** (0.042) (0.043) Expected loss 0.430*** 0.428** (0.127) (0.184) Expected gain 0.180*** 0.247* (0.050) (0.140) Short sale 0.368*** 0.381*** (0.112) (0.115) Log number of children 0.040 0.057 (0.037) (0.038) Quality −0.202*** −0.353** (0.072) (0.157) MLS listing 0.183*** 0.175*** (0.052) (0.053) Open house 0.386*** 0.390*** (0.040) (0.041) Internet marketing 0.269*** 0.262*** (0.074) (0.074) Magazine marketing 0.380*** 0.373*** (0.043) (0.043) Print marketing 0.175*** 0.172*** (0.042) (0.042) Sign in yard 0.111** 0.117** (0.052) (0.052) Detached SFR 0.283*** 0.284*** (0.063) (0.064) Suburban 0.005 −0.012 (0.068) (0.068) City −0.012 −0.007 (0.062) (0.063) Small town −0.158*** −0.162*** (0.041) (0.041) New home −0.202 −0.243 (0.238) (0.244) Old home 0.236 0.313** (0.150) (0.155) Small home −0.191 −0.194 (0.171) (0.172) Large home 0.153 0.168 (0.146) (0.146) Many bathrooms 0.436** 0.388** (0.184) (0.191) Hayunga

Table 7 (continued)

(1) includes concessions IV (2) uses non-IV concessions

Many bedrooms −0.316* −0.259 (0.186) (0.186) Inverse Mills ratio 2.932*** 2.818*** (0.839) (0.829) High urgency −0.032 (0.056) Some urgency 0.046 (0.040) Log square feet 0.151*** (0.057) Log number of earners 0.005 (0.097) 1–5 miles S.D. 0.002 (0.071) 6–10 miles S.D. 0.056 (0.076) 11–15 miles S.D. 0.102 (0.083) 16–20 miles S.D. 0.113 (0.093) 21–50 miles S.D. 0.189** (0.083) 51–100 miles S.D. 0.175 (0.119) 101–500 miles S.D. 0.008 (0.076) 501–1000 miles S.D. 0.080 (0.080) Holding period 0.005 (0.006) Log home age −0.036 (0.028) Constant 2.017*** 1.114 (0.584) (1.066) Observations 3270 3263 Adjusted R2 0.380 0.372 Robust score χ2 (p-value) 0.661 0.000 Partial R2 0.103 0.397 Robust F 25.86 104.8 Over-identification (p-value) 0.392 0.110

The models present determinants of log TOM and include fixed effects at the 3-digit ZIP code level and for the year of listing. Additional definitions of each variable are in Table 10. Heteroscedasticity-consistent and IV- robust standard errors in parentheses *** , ** and * denote p <0.01,p < 0.05, and p < 0.10 respectively Sales Concessions in the US Housing Market

Since the test statistics indicate that concessions is not endogenous, Model 2 in Table 7 adds the log price IV and the non-instrumented, observed values for concessions. As before, prices and TOM are strongly endogenous. The price IV is also quite strong and not over-identified. Consistent with search theory, prices exhibit a positive and significant relation with TOM. Similar to Model 1, the parameter estimate on the concessions variable is again positive and significant in Model 2. Additionally, as measured by adjusted R2, the model in Table 7 exhibits a strong fit not generally observed in TOM specifications. There are several other notable results in Table 7 that have not been previously reported in the housing literature. Selling to a friend and higher sellers’ income levels both decrease TOM. First time sellers and owners of higher quality homes also realize a reduced TOM, all else held equal. There are certain sellers who experience an increase in TOM. Despite the data in Table 4 demonstrating that African American owners realize lower transaction prices, they experience longer TOM. Also in Table 4, we see that sellers expecting losses upon sale will realize higher transaction prices, which can be expected given the higher list prices found by Genesove and Mayer (2001) and Hayunga and Pace (2016). We observe in Table 7 that these sellers also experience longer TOM, a finding consistent with search theory. Lastly, we note that the various marketing mechanisms exhibit positive slope coefficients with TOM. This was also evident in Table 4 along with positive increases in the use of concessions. Taken together, the results suggest that certain sellers are anxious to sell after being on the market longer than expected as evidenced by the TOM result. These sellers thus increase their marketing efforts and add concessions to increase the probability of sale. Recall though that these sales are not met with higher transactions prices. Previous test statistics demonstrate that prices, TOM, and concessions are determined simultaneously. The finding that concessions are not endogenous in the TOM model in model 1 is somewhat unexpected. In our next analysis, we see that the lack of endogeneity is partially explained by the different concession types.

Individual Concessions

Table 8 details the key parameters for specifications that include each incentive type individually. As before, the model in the first column withholds price to confirm the endogeneity test statistics. The results demonstrate that payments for closing costs and home warranties are endogenous and the IV is not weak. The home warranty IV is over-identified at the 10% level but not at the 5%. The IV for payment of repairs does not exhibit endogeneity with TOM. This helps explain the insignificant endogeneity test on the all-inclusive measure. Part of the reason for the non-endogenous result on repairs is that, despite many unique variables, it is a challenge to find high-quality instruments. Referring back to Table 2, we note that, relative to the other incentives, there are fewer significant determinants that alter the propensity to include payments for repairs. Further, our analysis finds that many of the determinants in Table 2 also correlate with Hayunga

Table 8 Log TOM including specific concession types

Withholds log prices System of equations

Panel A Closing costs IV −0.451 0.203 (0.298) (0.327) Log selling price IV 0.183** (0.089) Constant 2.995*** 0.867 (0.478) (1.161) Observations 3270 3263 Adjusted R2 0.328 0.467 Robust score χ2 (p-value) 0.011 Partial R2 0.024 Robust F 9.796 Over-identification (p-value) 0.250 Panel B Home warranty IV 0.539*** 0.028 (0.188) (0.302) Log selling price IV 0.154** (0.077) Constant 1.974*** 1.196 (0.557) (1.051) Observations 3270 3263 Adjusted R2 0.359 0.463 Robust score χ2 (p-value) 0.049 Partial R2 0.050 Robust F 13.14 Over-identification (p-value) 0.091 Panel C Repairs IV 0.300 0.158 (0.520) (0.566) Log selling price IV 0.142* (0.084) Constant 1.950*** 1.135 (0.595) (1.113) Observations 3270 3263 Adjusted R2 0.376 0.469 Robust score χ2 (p-value) 0.886 Partial R2 0.017 Robust F 7.402 Over-identification (p-value) 0.434

The table presents TOM models including each concession type. Heteroscedasticity-consistent and IV-robust standard errors in parentheses *** , ** and * denote p <0.01,p < 0.05, and p < 0.10 respectively Sales Concessions in the US Housing Market

Table 9 Log TOM split on relative bargaining power

All concessions Closing costs Home warranty Repairs

Panel A: Higher θ All concessions IV −0.729** (0.309) Closing costs IV −0.974* (0.554) Home warranty IV 0.143 (0.320) Repairs IV −1.148* (0.669) Constant 7.815*** 7.196*** 3.254* 5.505*** (1.623) (2.170) (1.858) (1.705) Observations 829 829 829 829 Adjusted R2 0.168 0.157 0.318 0.256 Panel B: Lower θ All concessions IV 0.432 (0.422) Closing costs IV −0.044 (0.416) Home warranty IV 0.686** (0.312) Repairs IV 1.002 (0.903) Constant 0.372 3.639*** 3.254** 4.665** (1.865) (1.287) (1.406) (2.182) Observations 792 792 792 792 Adjusted R2 0.393 0.386 0.313 0.339

The table reports the impact of concessions on log TOM after splitting the sample based on the Carrillo (2013) index of relative bargaining power (θ). Panel A presents the findings using transactions in the quartile with sellers having greater bargaining power while Panel B reports results using the transactions in the quartile with buyers having greater bargaining power. All specifications are part of a system of equations that controls for the endogeneity of both concessions and TOM. Heteroscedasticity-consistent and IV-robust standard errors in parentheses *** , ** and * denote p <0.01,p < 0.05, and p < 0.10 respectively

TOM. The F statistic in panel C of Table 8 accordingly is less than our desired threshold. In the structural equations that initially withhold log prices, the findings indicate that home warranty is the one concession that correlates with TOM. The positive coefficient indicates an increase in TOM when a home warranty is included in the transaction. The second specifications includes price along with each concession in a system of equations. We observe that price subsumes the impact of home Hayunga warranties on TOM, and that all three concessions are not predictive of TOM. This is similar to the prior transaction price findings.

Bargaining Power

W next consider the relative bargaining power between sellers and buyers with respect to TOM. Panel A of Table 9 reports the transactions when sellers have more bargaining power and Panel B presents the results when buyers have greater leverage. We again observe contrasting effects. In the higher θ markets, sellers are able to decrease their TOM when using concessions, specifically payments for closing costs and repairs. These findings suggest that the inclusion of a concession in a sellers’ market is an added feature. Conversely, in markets with lower θ, transactions including home warranties experience longer TOM. The correlation is consistent with owners experiencing longer TOM and then including home warranties as an added incentive.

Conclusion

This article presents new understanding of the motivations to use and the impact of concessions. The first contribution is to document aspects of the transaction that change the probability of concessions being included in a sale. We find that direct measures of sellers’ urgency levels do not change the propensity to utilize concessions. Instead, sellers who indicate that their properties are too expensive to keep increase the use of concessions, as do owners who expect to experience a loss upon sale. Other determinants that increase the probability of concessions being part of the transactions are longer holding periods and an older age of homes. Factors that decrease the propensity to use concessions are older sellers and transactions between acquaintances, as well as better structural quality. The other major contribution of this study is to analyze whether sellers can expect a reduction in TOM or an increase in prices when they include concessions. While the extant literature has attempted to address these research questions, there is limited credible evidence due to sample selection, omitted variables, and simultaneity. We use NAR data, strong instruments, and a system of simultaneous equations to mitigate these issues. Since concessions are known to be introduced during the bargaining phase, our tests also include a measure of relative bargaining power between buyers and sellers. The results demonstrate that negotiation leverage is an essential element in the effect concessions have on prices and TOM. In markets where sellers have greater bargaining power, the results demonstrate that homes with payments for closing costs and credits for repairs experience higher transaction prices and shorter TOM. In markets where buyers have greater bargaining power, homes that include home warranties and credits for repairs exhibit lower transaction prices. Transactions that include home warranties also experience longer TOM. Sales Concessions in the US Housing Market

Appendix 1

Table 10 Variable definitions

Variable Definition

Log price Natural logarithm of transaction prices. Log TOM Natural logarithm of the number of weeks the property is on the market. All concessions Binary variable that equals one if a transaction includes any one of the concessions types, and zero otherwise. Closing costs Binary variable that equals one if a transaction includes a payment of closing costs, and zero otherwise. Home warranty Binary variable that equals one if a transaction includes a home warranty, and zero otherwise. Repairs Binary variable that equals one if a transaction includes a credit for repairs, and zero otherwise. High urgency Binary variable that equals one if survey responders indicate a high level of urgency to sell, and zero otherwise. Some urgency Binary variable that equals one if survey responders indicate some level of urgency to sell, and zero otherwise. No urgency Binary variable that equals one if survey responders indicate no urgency to sell, and zero otherwise. Too expensive to Binary variable that equals one if survey responders indicate their reason to sell is that keep the home is too expensive to keep, and zero otherwise. Job relocation Binary variable that equals one if survey responders indicate their reason to sell is due to a job relocation, and zero otherwise. Family change Binary variable that equals one if survey responders indicate their reason to sell is due to a family change such as a divorce or the birth of a child, and zero otherwise. Avoid foreclosure Binary variable that equals one if survey responders indicate their reason to sell is to avoid foreclosure, and zero otherwise. Sold to a friend Binary variable that equals one if the home is sold to a friend, relative, or acquaintance, and zero otherwise. Income x–y($k) Binary variable that equal one when the survey responders indicate an income ($) between the ranges set as x and y on the NAR survey, and zero otherwise. The control group earns annual income less than $35k. Log sellers’ Continuous measure of natural logarithm of sellers’ income using the midpoint of each incomes category with the top-coded upper category set to $1.5 M. Ages x–y Binary variable that equal one when the survey responders indicate an age between the ranges set as x and y on the NAR survey, and zero otherwise. The control group has ages less than 30. Log sellers’ ages Continuous measure of natural logarithm of sellers’ age using the midpoint of each category with the top-coded upper category set to age 85. African American Binary variable that equals one if the seller is African American, and zero otherwise. Asian Binary variable that equals one if the seller is Asian, and zero otherwise. Hispanic Binary variable that equals one if the seller is Hispanic, and zero otherwise. Caucasian Binary variable that equals one if the seller is non-Hispanic, Caucasian race, and zero otherwise; the control group. First time seller Binary variable that equals one if the owner is a first time seller, and zero otherwise. Expected loss Following Genesove and Mayer (2001), a continuous variable that equals the purchase price minus the expected selling price, and zero otherwise. Hayunga

Table 10 (continued)

Variable Definition

Following Bokhari and Geltner (2011), a continuous variable that equals the purchase Expected gain price minus the expected selling price, and zero otherwise. Short sale Binary variable that equals one if the transaction is a short sale, and zero otherwise. Log number of Natural logarithm of the number of household earners. earners Log number of Natural logarithm of the number of children in the household. children x–y miles S.D. Binary variable equal to one if the number of miles between the sale and purchase properties are within the range from x to y, and zero otherwise. Control group is separation distance greater than 1000 miles. Log separation Continuous measure of natural logarithm of number of miles between the sale and distance purchase properties using the midpoint of each category. Quality Following Genesove and Mayer (2001), a measure of structural quality computed as the residuals from a hedonic model of sold properties within the sample at the time of purchase. Holding period The number of years between the purchase and the sale of the home. Log square feet Natural logarithm of the number of square feet in the home. Log home age Natural logarithm of the home age. MLS listing Binary variable that equals one if the home is listed in MLS, and zero otherwise. Open house Binary variable that equals one if the home is marketed using an open house, and zero otherwise. Internet marketing Binary variable that equals one if the home is marketed using the Internet, and zero otherwise. Magazine Binary variable that equals one if the home is marketed using magazine advertising, and marketing zero otherwise. Print marketing Binary variable that equals one if the home is marketed using print advertising, and zero otherwise. Sign in yard Binary variable that equals one if the home is marketed using a sign in the yard, and zero otherwise. Detached SFR Binary variable that equals one if the home is detached single-family residence, and zero otherwise. Suburban Binary variable that equals one if the home is located in the suburbs, and zero otherwise. City Binary variable that equals one if the home is located in a city, and zero otherwise. Small town Binary variable that equals one if the home is located in a small town, and zero otherwise. Resort property Binary variable that equals one if the home is a resort property, and zero otherwise. New home Binary variable that equals one if the home age is 2 years or less, and zero otherwise. Old home Binary variable that equals one if the home age is 120 years or more, and zero otherwise. Small home Binary variable that equals one if the home is less than 900 square feet, and zero otherwise. Large home Binary variable that equals one if the home is more than 5000 square feet, and zero otherwise. Many bathrooms Binary variable that equals one if the home has more than 5 bathrooms, and zero otherwise. Many bedrooms Binary variable that equals one if the home has more than 6 bedrooms, and zero otherwise. Sales Concessions in the US Housing Market

Appendix 2: Sample Selection

Since the NAR survey is sent to both buyers and sellers, we can potentially control for sample selection bias resulting from modeling only homes that sell. A small percentage of the buyers have purchased a new home but have not sold their previous residence. The unsold properties occur in each survey year. These responders may have their home listed but may be less motivated to sell and thus have unique price-TOM preferences. We control for potential selection bias using the traditional Heckman two-step procedure and compute the IMR. The first step estimates a probit model of whether the house has sold or not. Table 10 reports the specification modeling propensity to sell with sold properties set equal to one and properties that have not sold set equal to zero. From this specification, we obtain the IMR. Consistent with having unique price-TOM preferences, the results demonstrate that the computed IMR is significantly correlated with TOM in the second stage structural equations.

Table 11 Propensity to sell

Parameter estimate Standard error

High urgency −0.672 (0.473) Some urgency −0.471 (0.448) Job relocation −0.453 (0.451) Log sellers’ incomes 0.372 (0.281) Log seller’sages −0.650 (0.788) Sold to friend 0.002 (0.558) First time seller −0.527 (0.339) Short sale −0.905* (0.492) Log number of earners 0.507 (0.756) Log number of children 0.039 (0.367) Log separation distance 0.034 (0.076) Log square feet −0.143 (0.464) Log home age 0.069 (0.212) Suburban −0.594 (0.374) Detached SFR −5.256** (2.126) MLS listing 0.103 (0.404) Open house 0.061 (0.344) Internet marketing −0.700 (0.895) Magazine marketing 0.291 (0.416) Print marketing −0.218 (0.340) Sign in yard 0.442 (0.386) Year of listing 2010 −4.081*** (0.740) Year of listing 2011 −3.481*** (0.752) Year of listing 2012 −3.643*** (0.739) Constant 11.738*** (2.126) McFadden’s LRI 0.250

The table reports the first stage of the Heckman two-step procedure that corrects for sample selection bias and computes the inverse Mills ratio *** , ** and * denote p <0.01,p < 0.05, and p < 0.10 respectively Hayunga

Appendix 3: Linear Probability Models

To avoid the forbidden regression and the incidental parameters problems, we use linear probability models to compute the concession IVs. Nonlinear models such as probit cannot be used in the first stage of a two-stage specification as neither the conditional expectations operator nor the linear projection carry through nonlinear functions. Thus, the forbidden regression makes the error of replacing a nonlinear function of an endogenous explanatory variable with the same nonlinear function of fitted values from a first-stage estimation (See Angrist and Pischke 2008;andWooldridge2010). Put differently, the problem is that the consistency property of 2SLS in the fully linear models does not generally extend to the use of nonlinear models. The incidental parameters problem arises with limited dependent variables such as binary concession measures. When fitting linear models, fixed effects correctly measure the mean value of the dependent variable for a particular attribute. These are spatial and temporal controls in our empirical models. However, as the number of fixed effects becomes large, econometric estimators fail to converge toward consistent estimates and their slope coefficients become biased (Neyman and Scott 1948). Since we have a significant number of locational and annual fixed effects, approximately 450 in most models, we use the linear probability model to avoid the incidental parameters problem. To confirm that our results are not driven by the use of linear probability models, we provide the full specifications in Table 12, which can be compared to the probit models in Table 2. We observe that the sign, magnitude, and significance are consistently similar across the models in both tables.

Table 12 Linear probability models

All Closing Home concessions costs warranty Repairs

High urgency 0.012 −0.004 −0.001 0.014 (0.026) (0.022) (0.023) (0.015) Some urgency 0.007 0.011 −0.011 −0.000 (0.019) (0.016) (0.017) (0.010) Too expensive to keep 0.187*** 0.041 0.144*** 0.023 (0.053) (0.040) (0.048) (0.029) Job relocation −0.070** −0.037 −0.042* 0.025 (0.029) (0.024) (0.025) (0.016) Family change −0.037 −0.038 −0.037 0.015 (0.031) (0.025) (0.026) (0.016) Avoid foreclosure −0.081* −0.073** −0.062* −0.025 (0.043) (0.033) (0.033) (0.019) Sold to a friend −0.133*** −0.092*** −0.061** −0.027** (0.032) (0.024) (0.025) (0.013) Log sellers’ income −0.029* −0.018 −0.016 −0.002 (0.015) (0.012) (0.013) (0.009) Log seller's age −0.205*** −0.177*** −0.112** 0.007 (0.051) (0.044) (0.045) (0.028) Sales Concessions in the US Housing Market

Table 12 (continued)

All Closing Home concessions costs warranty Repairs

African American 0.123* 0.132** 0.013 0.026 (0.068) (0.065) (0.060) (0.043) Asian −0.025 0.004 −0.034 −0.007 (0.058) (0.050) (0.046) (0.029) Hispanic 0.035 −0.021 0.012 −0.005 (0.057) (0.044) (0.048) (0.030) First time seller −0.026 0.019 −0.011 0.002 (0.022) (0.019) (0.019) (0.012) Expected loss 0.682*** 0.345*** 0.370*** 0.110*** (0.074) (0.058) (0.061) (0.037) Expected gain 0.609*** 0.282*** 0.397*** 0.107*** (0.058) (0.047) (0.043) (0.029) Short sale −0.228*** −0.108*** −0.160*** −0.032 (0.048) (0.038) (0.038) (0.022) Log number of earners −0.091** −0.017 −0.047 −0.032 (0.045) (0.036) (0.039) (0.025) Log number of children −0.003 −0.015 −0.003 −0.003 (0.019) (0.016) (0.017) (0.010) Log separation distance 0.014*** 0.006 0.007* −0.001 (0.005) (0.004) (0.004) (0.003) Quality −0.699*** −0.370*** −0.410*** −0.085*** (0.060) (0.049) (0.047) (0.029) Holding period 0.029*** 0.015*** 0.018*** 0.005*** (0.003) (0.002) (0.002) (0.001) Log square feet −0.031 −0.082*** 0.046* 0.037** (0.029) (0.024) (0.025) (0.016) Log home age 0.033** 0.012 0.027** 0.027*** (0.013) (0.011) (0.011) (0.007) MLS listing 0.090*** 0.067*** 0.068*** 0.009 (0.025) (0.020) (0.021) (0.014) Open house 0.049** 0.015 0.032* 0.041*** (0.019) (0.016) (0.017) (0.011) Internet marketing 0.114*** 0.046* 0.078*** 0.018 (0.032) (0.025) (0.025) (0.016) Print marketing 0.011 −0.009 −0.013 0.009 (0.021) (0.017) (0.018) (0.012) Sign in yard 0.070*** 0.036* 0.074*** −0.000 (0.023) (0.019) (0.019) (0.013) Detached SFR −0.023 0.023 −0.015 −0.016 (0.028) (0.024) (0.025) (0.015) Suburban 0.023 0.020 0.025 0.040** (0.032) (0.027) (0.028) (0.020) City 0.006 0.019 −0.010 0.019 (0.030) (0.025) (0.025) (0.017) Small town 0.018 0.007 0.018 −0.002 (0.019) (0.016) (0.017) (0.010) Hayunga

Table 12 (continued)

All Closing Home concessions costs warranty Repairs

Resort property 0.050 −0.046 0.016 0.111* (0.085) (0.049) (0.064) (0.062) New home 0.075 0.068 0.024 (0.116) (0.090) (0.083) Old home −0.119 −0.080 −0.100* −0.053 (0.072) (0.051) (0.058) (0.043) Small home −0.132* −0.105* −0.157*** 0.057 (0.069) (0.062) (0.053) (0.050) Large home 0.017 −0.070* −0.029 0.041 (0.071) (0.042) (0.060) (0.052) Many bathrooms 0.106 0.033 0.015 −0.053 (0.094) (0.055) (0.086) (0.057) Many bedrooms −0.139* 0.018 −0.060 0.051 (0.085) (0.053) (0.070) (0.069) Constant 1.373*** 1.442*** 0.091 −0.261 (0.333) (0.281) (0.290) (0.201) Observations 3302 3302 3302 3302 Adjusted R2 0.131 0.084 0.113 0.024

The first column equals one if any one of the concessions is included in the transaction and zero otherwise. Heteroscedasticity-consistent standard errors in parentheses *** , ** and * denote p <0.01,p < 0.05, and p < 0.10 respectively

Appendix 4: Reduced Form Equations

Table 13 First stage equations used in price models

Log TOM Standard error All incentives Standard error

Instruments Detached SFR 0.285*** (0.070) Inverse Mills ratio 3.174*** (0.920) Sold to a friend −0.372*** (0.091) −0.110*** (0.036) MLS listing 0.198*** (0.058) 0.150*** (0.027) Open house 0.411*** (0.045) 0.121*** (0.021) Internet marketing 0.297*** (0.081) 0.135*** (0.035) Magazine marketing 0.376*** (0.047) 0.015 (0.023) Print marketing 0.170*** (0.046) 0.009 (0.023) Sign in yard 0.135** (0.056) 0.062** (0.026) First time seller −0.076*** (0.024) Log home age 0.076*** (0.015) Exogenous variables Sales Concessions in the US Housing Market

Table 13 (continued)

Log TOM Standard error All incentives Standard error

High urgency −0.036 (0.062) −0.003 (0.029) Some urgency 0.040 (0.045) −0.015 (0.021) Too expensive 0.298** (0.138) 0.379*** (0.063) Job relocation −0.046 (0.071) −0.086** (0.034) Family change 0.112 (0.074) 0.002 (0.034) Avoid foreclosure −0.079 (0.097) −0.039 (0.044) Income 35–44k 0.001 (0.146) 0.004 (0.064) Income 45–54k 0.131 (0.137) 0.010 (0.061) Income 55–64k −0.058 (0.132) 0.143** (0.062) Income 65–74k −0.046 (0.131) 0.037 (0.057) Income 75–84k −0.190 (0.129) 0.026 (0.056) Income 85–99k −0.112 (0.127) 0.052 (0.055) Income 100–124k −0.169 (0.121) 0.053 (0.053) Income 125–149k −0.259** (0.127) 0.006 (0.055) Income 150–174k −0.320** (0.133) −0.037 (0.060) Income 175–199k −0.276* (0.144) −0.001 (0.063) Income 200–249k −0.397*** (0.141) 0.030 (0.062) Income 250–499k −0.329** (0.140) −0.008 (0.062) Income 500–999k −0.107 (0.196) −0.081 (0.101) Income 1,000k + −0.735 (0.485) 0.254 (0.157) Ages 30–34 0.046 (0.104) −0.119** (0.051) Ages 35–30 0.144 (0.105) −0.204*** (0.052) Ages 40–44 0.080 (0.109) −0.231*** (0.054) Ages 45–50 −0.039 (0.114) −0.266*** (0.056) Ages 50–54 0.106 (0.119) −0.303*** (0.057) Ages 55–59 0.139 (0.122) −0.372*** (0.061) Ages 60–64 0.179 (0.128) −0.372*** (0.062) Ages 65–69 0.034 (0.137) −0.433*** (0.065) Ages 70–74 0.128 (0.154) −0.423*** (0.074) Ages 75–79 0.227 (0.184) −0.336*** (0.091) Ages 80+ 0.368* (0.212) −0.487*** (0.097) African American 0.439*** (0.155) 0.140* (0.076) Asian 0.014 (0.134) −0.006 (0.063) Hispanic −0.013 (0.118) 0.046 (0.060) First time seller −0.080 (0.051) 1.524*** (0.124) Expected loss 0.670*** (0.200) 1.316*** (0.111) Expected gain 0.421*** (0.155) −0.286*** (0.057) Short sale 0.312*** (0.122) −0.336*** (0.052) Log number of earners −0.015 (0.110) 0.020*** (0.023) Log number of children 0.081 (0.050) −0.156 (0.037) 1–5 miles S.D. −0.027 (0.077) −0.092*** (0.039) 6–10 miles S.D. 0.036 (0.081) −0.080** (0.043) 11–15 miles S.D. 0.092 (0.090) −0.082* (0.046) 16–20 miles S.D. 0.096 (0.101) −0.031* (0.042) Hayunga

Table 13 (continued)

Log TOM Standard error All incentives Standard error

21–50 miles S.D. 0.182** (0.090) −0.038 (0.057) 51–100 miles S.D. 0.169 (0.130) −0.014 (0.039) 101–500 miles S.D. 0.002 (0.081) −0.049 (0.041) 501–1000 miles S.D. 0.068 (0.087) −1.439 (0.112) Quality −0.455*** (0.163) 0.063*** (0.005) Holding period 0.011 (0.008) −0.003*** (0.031) Log square feet 0.119* (0.068) −0.003 (0.029) Log home age −0.024 (0.030) Suburban 0.012 (0.076) 0.069** (0.035) City 0.007 (0.069) 0.055* (0.033) Small town −0.147*** (0.045) 0.023 (0.021) Resort property 0.045 (0.220) 0.276*** (0.088) New home −0.248 (0.274) 0.263** (0.126) Old home 0.236 (0.171) −0.193*** (0.075) Small home −0.205 (0.184) −0.156* (0.080) Large home 0.166 (0.159) −0.055 (0.083) Many bathrooms 0.425** (0.207) 0.112 (0.095) Many bedrooms −0.320 (0.206) −0.109 (0.099) Detached SFR −0.042 (0.031) Inverse Mills ratio 0.732 (0.477) Constant 2.075*** (0.714) 0.415 (0.323) Observations 3263 3263 Adjusted R2 0.373 0.186

The table reports the first stage equations to compute the predicted values of TOM and concessions to use in the price structural equations in Table 3. Standard errors are robust to heteroscedasticity *** , ** and * denote p <0.01,p < 0.05, and p < 0.10 respectively

Table 14 First stage equations used in TOM models

All incentives Standard error Log price Standard error

Instruments Log sellers’ ages −0.426*** 0.058 Log number of earners −0.334*** 0.053 Holding period 0.063*** 0.005 Log separation distance 0.023*** 0.005 Log home age 0.074*** 0.015 Resort 0.288*** 0.088 0.292*** 0.070 High urgency −0.084*** 0.018 Some urgency −0.068*** 0.014 Ages 30–34 0.003 0.028 Ages 35–30 0.006 0.031 Sales Concessions in the US Housing Market

Table 14 (continued)

All incentives Standard error Log price Standard error

Ages 40–44 0.075** 0.032 Ages 45–50 0.073** 0.034 Ages 50–54 0.083** 0.037 Ages 55–59 0.050 0.040 Ages 60–64 0.103** 0.040 Ages 65–69 0.136*** 0.043 Ages 70–74 0.080 0.054 Ages 75–79 0.137** 0.062 Ages 80+ 0.088 0.076 Log square feet 0.746*** 0.021 Exogenous variables High urgency −0.003 0.029 Some urgency −0.016 0.021 Too expensive 0.374*** 0.063 0.126*** 0.042 Job relocation −0.084** 0.033 −0.069*** 0.020 Family change −0.001 0.033 0.040* 0.022 Avoid foreclosure −0.038 0.044 −0.007 0.034 Sold to a friend −0.108*** 0.037 −0.023 0.029 Income 35–44k 0.007 0.064 0.077 0.052 Income 45–54k 0.005 0.061 0.056 0.046 Income 55–64k 0.134** 0.061 0.112** 0.044 Income 65–74k 0.025 0.057 0.182*** 0.043 Income 75–84k 0.016 0.056 0.191*** 0.041 Income 85–99k 0.039 0.055 0.212*** 0.044 Income 100–124k 0.043 0.053 0.264*** 0.042 Income 125–149k −0.005 0.055 0.300*** 0.044 Income 150–174k −0.054 0.059 0.331*** 0.046 Income 175–199k −0.016 0.063 0.360*** 0.046 Income 200–249k 0.015 0.061 0.402*** 0.047 Income 250–499k −0.025 0.062 0.497*** 0.046 Income 500–999k −0.101 0.098 0.566*** 0.060 Income 1,000k + 0.251 0.163 0.673*** 0.083 African American 0.137* 0.077 −0.239*** 0.057 Asian −0.005 0.063 0.103*** 0.038 Hispanic 0.044 0.060 −0.012 0.043 First time seller −0.072*** 0.024 −0.010 0.017 Expected loss 1.507*** 0.126 0.324*** 0.098 Expected gain 1.299*** 0.114 −0.067 0.083 Short sale −0.288*** 0.057 −0.217*** 0.040 Log number of children 0.011 0.020 −0.011 0.014 Log number of earners −0.056 0.037 1–5 miles S.D. −0.073*** 0.025 Hayunga

Table 14 (continued)

All incentives Standard error Log price Standard error

6–10 miles S.D. −0.104*** 0.026 11–15 miles S.D. −0.089*** 0.028 16–20 miles S.D. −0.126*** 0.029 21–50 miles S.D. −0.091*** 0.027 51–100 miles S.D. −0.072** 0.034 101–500 miles S.D. −0.056** 0.025 501–1000 miles S.D. 0.009 0.026 Quality −1.425*** 0.114 0.642*** 0.087 Log square feet 0.000 0.031 Log home age 0.001 0.009 Detached SFR −0.044 0.031 0.000 0.019 MLS listing 0.145*** 0.026 0.026 0.018 Open house 0.120*** 0.022 0.038** 0.014 Internet marketing 0.136*** 0.035 0.018 0.026 Magazine marketing 0.015 0.023 0.002** 0.013 Print marketing 0.009 0.023 0.029 0.013 Sign in yard 0.060** 0.025 0.001*** 0.016 Suburban 0.067* 0.034 0.051** 0.023 City 0.052 0.033 0.034*** 0.021 Small town 0.021 0.021 0.041 0.013 New home 0.253 0.127 0.138** 0.064 Old home −0.188** 0.075 −0.161*** 0.054 Small home −0.160** 0.079 0.044 0.069 Large home −0.050 0.084 −0.061 0.062 Many bathrooms 0.126 0.095 0.304*** 0.063 Many bedrooms −0.110 0.098 −0.104 0.066 Inverse Mills ratio 0.669 0.474 0.212 0.293 All incentives −0.044*** 0.013 Constant 1.736*** 0.378 6.711 0.200 Observations 3263 3263 Adjusted R2 0.187 0.806

The table reports the first stage equations to compute the predicted values of prices and concessions to use in the TOM structural equations in Table 7. Standard errors are robust to heteroscedasticity *** , ** and * denote p <0.01,p < 0.05, and p < 0.10 respectively

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