The Impact of Risk on the Price of Residential Properties: The Case of England Philippe Bélanger & Michael Bourdeau-Brien 1 Department of Finance, Insurance and Real Estate, Université Laval Email: [email protected] [email protected]

Areas / Paper type – Change and Risk - Issues for Property Valuation work?

ABSTRACT

This paper examines the impact of flood risk on the value of England residential properties. We find that being located within a flood zone significantly lowers property values once we control for the proximity to a watercourse that often increases house prices. Interestingly, the effect of flood risk is predominantly associated with the post-2003 period which can be rationalized by changes in insurance practices and availability of detailed information on flood zones. Moreover, people in richer areas appear to better incorporate available flood risk data while people in poorer areas seem to associate flood risk with proximity to the water.

Keywords: ; Real Estate; Housing; Household behaviour

JEL Classification: D12, H31, Q54, R31

Proceedings: ERES2016 pp. xx-xx

1. Introduction Change and Risk - Issues for Property Valuation Floods and other major natural hazards have a far-reaching impact on the economies of work? affected regions. According to a 2015 study from the United Nations’ Food and Agriculture Organization1, natural disasters caused more than $1.5 trillion in damage 2 and 1.1 million deaths worldwide between 2003 and 2013. These numbers may yet get worse because of global warming that tends to increase the frequency and intensity of extreme weather events (Francis and Vavrus, 2012, Rahmstorf and Coumou, 2011, Douglas et al., 2010, Kazmierczak and Bichard, 2010, Thorne et al., 2007). Still, the negative effects of catastrophes are often circumscribed to emerging countries (Mechler, 2009) and studies focussing on developed economies obtain mixed results. On the one hand, many papers report a decline in economic growth (Hochrainer, 2009, Raddatz, 2009, Strobl, 2011, Noy and Nualsri, 2011). On the other hand, an almost equal number of studies see extreme weather events having a neutral or positive effect on productivity (Skidmore and Toya, 2002, Leiter et al., 2009, Baker and Bloom, 2013, Bernile et al., 2015). We observe similar conflicting conclusions in the housing markets literature.

On the negative side, Shillings, Sirmans and Benjamin (1989) study the effect of flood risk on residential houses in Baton Rouge, Louisiana, and observe that the risk of being flooded significantly lowers the selling price of a house. Harrison, Smersh and Schwartz (2001) examine the valuation of homes in Alachua County, Florida, and find evidence that houses located within a flood zone sell for less than homes located elsewhere. Bin, Kruse and Landry (2008) investigate the effect of flood risk on property values in Carteret County, North Carolina, and also show a meaningful negative premium for houses located in flood-prone localities. On the neutral or positive side, Small, Newby and Clarkson (2013) compare the actual performance of the housing market before and after the 2011 flood in Rockhampton, Australia, and fail to notice any impact on house values. Zimmerman (1979) studies the effect of a floodplain location on home in three towns in New Jersey and finds no variation for flood prone and non-flood prone lands. Bialaszewski and Newsome (1990) run a similar study on residential properties in Homewood, Alabama and detect no effect associated with a location within a floodplain location. Last, Morgan (2007) observes that houses in Santa Rosa County, Florida, that are located within a floodplain benefit from a positive premium.

The most popular explanation that has been put forward to rationalise the dichotomous results is associated with risk awareness and imperfect information. Lamond and Proverbs (2006) and Lamond, Proverbs and Hammond (2010) empirically show that flood-relate premiums in are essentially associated with flood events and not flood risk per se and that the premiums slowly fade away as flood episodes are forgotten. Others obtain similar findings for Netherland, United States and Australia (Husby et al., 2014, Bin and Landry, 2013, Atreya et al., 2013, McKenzie and Levendis, 2010, Bin and Polasky, 2004, Eves, 2002, Harrison et al., 2001). Pryce, Chen and Galster (2011) show how market participant’s myopia and amnesia behaviours

1 The Impact of Disaters on Agriculture and Food Security. Available at http://www.fao.org/3/a-i5128e.pdf. (page consulted on May 30, 2016).

affect the perception of flood risk through a theoretical model and discuss how and when location within flood-prone areas are expected to lead to substantial premiums. Other plausible explanations for the divergent impact of flood risk on housing include Daniel, Florax and Rietveld (2009) who argue that previous studies often fail to adequately take into account the positive effect of a location close to the water and that the literature would benefit from alternative methodologies that better incorporate this 3 confounding variable and Fielding (2007) who observes an unequal distribution of households in floodplains with respect to their social class in the United Kingdom where poorer households have a higher propensity to live in flood zones than wealthier families. Moreover, one limitation of most previous papers derives from their rather small geographic coverage, usually confined to one or a few counties or municipalities located in a given region that may be susceptible to share a common urban organisation and similar housing market characteristics. Hence, it is unclear if previous findings correctly portray the general housing market and if the results are of interest to regional or national authorities and geographically diversified insurance companies.

This paper addresses this shortcoming and investigates the effect of flood risk on residential property values spread across multiple localities in England. We perform our analysis using a sample of over 100,000 transactions from the U.K. Land Registry between 1995 and 2015. We use available geocoding services to locate individual properties. We superpose several layers of geographical data in order to distinguish between properties within and outside flood zones, to calculate distances from the nearest body of water and to group properties on the basis of census-based output area boundaries defined in a way that recognises homogeneous environments. Building on existing literature, our approach allows us to control for the confounding effects associated with the proximity to the water and to control for the interaction between flood risk and levels of economic deprivation.

Yet, expanding the geographic coverage to hundreds of localities comes with a price. We are no longer able to obtain enough individual house characteristics to employ a classic hedonic model and must resort to another, less frequently used, econometric framework. We opt for a linear mixed-effects model that permits to take into account the expected correlation between house prices in each small local area. We make sure our inferences are not biased by heteroscedasticity or by departure from the normality in the residuals using a the wild bootstrap methodology of Liu (1988) to assess the statistical significance of our regressors. We benchmark our main results by examining the effect of flood risk on various subsamples and implement some additional robustness checks.

Our findings indicate that location within a flood zone commands a price premium but that the distribution of the premium is contingent on the proximity to the water and on the area wealth. In high wealth areas, households appear to rely on official information on flood zones. Properties throughout flood zones exhibit lower house values and the price discount is of about 1.5 percent in average. Still, the negative effect of flood risk is more than offset by the positive premium of being located close to the water. Without an effective control for that countervailing variable, neither the proximity to

Change and Risk the water nor flood risk would appear as significant. The negative effect of flood risk is even more obvious in economically deprived areas where the drop in value exceeds 2 - Issues for percent. Opposing the situation in wealthier localities, lower house prices are only Property Valuation work? observed in the immediate vicinity of a body of water.

4 These results suggest that the myopia behaviour, or more generally the issues related with incomplete information, may be restricted to poorer neighbourhoods and hint that the U.K. flood awareness campaign that follows the major floods of 2000 better succeeded in rich than poor regions. The findings also strongly emphases the need to adequately control for the proximity to the water and for the distribution of wealth when examining the effect of floods on housing as these factors act as effective confounding variables.

The rest of the paper is structured as follows: Section 2 portrays the England housing market and discusses household flood risk awareness. Section 3 describes the linear mixed-effects model approach and the data. Section 4 presents and discusses the main results. Section 5 concludes.

2. Housing Market, Flood Risk and Risk Awareness in England

The England housing market is relatively mature. In some urban regions such as London, house prices are often considered as out of reach for first buyers2. These high prices underline an enduring demand imbalance as England new housing offer do not meet the need of population growth. Thus, it is not surprising that only 5% of the transactions involve the purchase of new dwellings. Moreover, England is characterized by the fact that several large urban centres are partially located within floodplains. Lamond et al. (2010) discuss extensively the effects of floods on the UK housing market and argue that the consequences of floods are expected to rise in the future. Hence, urban planning issues are directly connected to flood risk and this situation has many policy implications.

One of the direct impacts of flood risk materializes through a possibly lower property tax base if flood risk decreases property assessment. Beyond lower property tax revenues, knowing the impact of flood risk on house values is also important for governmental authorities in the decision process to implement flood mitigation measures. Indeed, the cost of those measures ought to be compared to the expected benefits in order to optimize risk management decision making. In addition, Penning- Rosswell and Pardoe (2012) claim that benefits originating from risk management investments such as the construction of flood defence infrastructures are often circumscribed to a rather limited area while their costs are spread across a large number of residents. That situation generates wealth transfers and may cause social tension between newly protected house owners, insurers and other taxpayers. According to the

2 e.g. “London House Prices Jump 5.4%, Locking Out First Time Buyers”, Bloomberg, Feb 14th, 2016; “Millions give up on home ownership as house prices soar”, The Telegraph, Dec 19, 2015; “UK house prices hit new record as London average breaks £500,000”, TheGardian, Sept. 16th, 2014.

Association of British Insurers (2002, 2005, 2008), insurance companies have been progressively removing nearly universal cover for flood risk from high risk properties since 2000 and now price insurance policies according to individual property flood risk. In spite of a more risk-based pricing of insurance policies, Penning-Rosswell and Pardoe (2012) observe no change in insurance premiums following the implementation of risk mitigation measures and conclude that insurance companies capture the benefit 5 associated with flood defences.

In England, the supervision of flood risk management lies mainly on the Environment agency. To stress the importance put on flood management, the Environment agency allocates about 65 percent of its £1.3 billion budget on flood and erosion risk management 3 . Improving households’ risk awareness is central to the Environment Agency mission. The agency is running major information programs since the major flood episodes of 1998 and 2000. Flood awareness programs consist of campaigns and tools to inform people of the inherent flood risk and, among other things, lead to the publication of detailed maps of flood zones by small areas. We believe that our investigation of the effect of flood risk on house values throughout England yields conclusions of particular interest for the Environment Agency and other organisations in the risk management industry. As a matter of fact, our study brings some light on how household behaviours change through time as new flood information is made available and pinpoints classes of households that would best benefit from additional awareness programs.

3. Method and Data

3.1 Econometric approach

Previous papers that examine the effect of floods on property valuation often rely on the classic hedonic framework (Harrison et al., 2001, Bin and Polasky, 2004, Bin and Kruse, 2006, Guttery et al., 2004) and distinguish high risk areas by including dummy variables. As hedonic models require data on individual houses’ characteristics, previous studies essentially limit their scope to one or a few small geographic localities. Hence, conclusions could be affected by regional housing market considerations.

Opposing most studies, we investigate the effect of flood risk on property values throughout England. Our dataset includes some individual house characteristics such as the building type, the tenure type, a new property indicator, the exact address and the geographic coordinates. However, the absence of a complete set of property specific descriptors prevents us from employing a hedonic pricing approach. Instead, we assume that houses present homogeneous characteristics by building type and by lower layer super output area (LSOA) and account for area-specific factors with LSOA-based

3 Environment Agency’s Annual report and accounts for the financial year 2014- 2015. Report available at https://www.gov.uk/government/publications/environment- agency-annual-report-and-accounts-2014-to-2015. Page consulted on May 28, 2016.

Change and Risk statistics. LSOA are small geographic areas that group continuous output areas that constituted the U.K. Census’ base geographical units. LSOA are built to improve the - Issues for reporting of small-area statistics. They are designed to have similar population size and Property Valuation work? be as socially homogeneous as possible based on tenure type and dwelling type. Each LSOA comprises between 1,000 and 3,000 inhabitants and contains between 400 and 6 1,200 households. England land is partitioned in a little more than 32,000 LSOA. The assumption of housing homogeneity by small area rests on the shared benefits and drawbacks of a similar location and on often comparable houses’ ages and building styles. Furthermore, that assumption is comforted by the fact that the coefficient of variation in house prices by LSOA is much lower than that by municipality, district or by broader geographic areas.

The methodology we employ in this study relies heavily on the literature on linear mixed effects models (LMM) characterised by the unbalanced, hierarchical and/or clustered structure of the data (Verbeke and Molenberghs, 2009, West et al., 2014). LMM are deemed appropriate in our context as the prices of individual properties of the same building type located within the same area cannot be assumed to be independent from each other. While LSOA statistics possibly help to explain the variations in valuation, there always remain a non-negligible part of cluster-specific effects that is unobservable and can result in an omitted-variable bias. In LMM, unobservable effects are treated like random effects (RE) for which the variance is estimated to improve inference on the coefficients of the observable regressors called fixed effects (FE). Treating unobserved effects as RE rather than as FE is deemed preferable as the RE allow time variations in the value of omitted variables while FE would restrict the omitted variables to be time-invariant. Also, the use of RE allows for the inclusion of time invariant LSOA statistics as control variables whereas no such variable can be included in a model with LSOA-based FE.

The following general equation describes the structure of our model:

푌 = 퐶훿 + 푋훽 + 푍푢 + 휖 (1)

Where Yn×1is the vector of responses, Cn×p is the matrix of control variables that

includes a constant term, δp×1 consists in the coefficients of the control variables, Xn×q

is the matrix of flood-related variables and βq×1 contains the main parameters of interest that convey the effect of flood risk on the response variable. Note that 퐶 and 푋

are both observable regressors and constitute the fixed effect design matrix. uk×1 is an

unknown vector of random effects, Zn×k is the random effects design matrix and ϵn×1 is

the vector of random errors. We assume that u~Νk(0, Σ) and restrict Σk×k to be a diagonal matrix. We also assume that the random errors ϵ~Ν(0, σ2). Unlike in OLS regression, the residuals associated with observations on the same cluster can be correlated. Last, our analysis is performed at the individual transaction level. Multiples transactions on the same houses are definitely not independent observations within a LSOA. We alleviate this concern by putting equal weight on each property. In other

words, each observation is weighted 1⁄푡푖 where 푡푖 is the number of transaction on house 푖.

Normally distributed LMM models are generally fitted using the method of maximum likelihood (ML). However Verbeke and Molenberghs (2009) argue that restricted maximum likelihood (REML) estimation is preferable to ML because it takes into account the loss of the degrees of freedom involved in estimating the fixed effects. Moreover, the estimation of the random effects under REML is less sensitive to 7 outliers compared to ML estimates. Accordingly we use REML to fit our model.

The main takeaways from our study lay on inferences drawn from the flood- related fixed effects. In this respect, Zeger, Liang and Albert (1988) assert that correct specification of the random effects distribution is not required in order to obtain valid estimates for the coefficients of the fixed effects in a LMM. However, correct specification of the fixed and random effects is needed to get the proper standard errors of the estimators. To address this issue, we do not run formal distributional tests for the random effects but instead employ the wild bootstrap (WB) approach developed by Liu (1988) to assess the statistical significance of our regressors. The WB is a semi- parametric procedure that leaves the regressors intact and resamples the response variable based on the residual values assuming that the null hypothesis is true. As the I.I.D. assumption is not imposed to simulate WB errors, it is well suit for models that exhibit heteroscedasticity and can also accommodate skewness in the residuals. The procedure we follow to implement the WB in the context of a LMM is motivated by Lombardia and Sperlich (2008) and carries on as:

Step 1: Estimate the LMM that incorporates the constraint of the null hypothesis. 2 Step 2: Generate a vector 푤1,푛×1 with 퐸[푤1] = 0 and 퐸[푤1 ] = 1. ∗ Construct the vector 휖 = 휖̂푤1.

Step 3: For each random effects term, generate a vector 푤1+푙,푠푙×1, 푙 = 1, … , 퐿 where 퐿 is the

number or random effect terms and 푠푙 is the number of groups in the random 2 effects term 푙. Again, we require that 퐸[푤1+푙] = 0 and 퐸[푤1+푙] = 1. Construct the ∗ vectors 푢푙 = 푢̂푙푤1+푙. ∗ ̂ ̂ ∗ ∗ Step 4: Under 퐻0 true, calculate the resampled response variable 푌 = 퐶훿 + 푋훽 + 푍푢 + 휖 . Step 5: Refit the LMM from the bootstrap sample ((푌∗, 퐶, 푋) and calculate the test statistic 휏∗. Step 6: Repeat steps 2 to 5 B times and compute the test’s p-value as 푝푣푎푙 = 1 ∑퐵 퐼(휏∗ ≥ 휏̂) where 휏̂ is the actual test statistic value and 퐼(∙) denotes the 퐵 푏 푏 indicator function that equals one when its argument is true and zero otherwise.

Several distributions can be used to generate the random vectors 푤. While all choices mandate to match the first and second moment of the error distribution, a trade- off must be made between correctly obtaining the third or the fourth moment. Davidson and Flachaire (2008) study that problem and provide evidence of the superior performance of Rademacher variables that impose symmetry even with skewed disturbances. Therefore, we choose to use Rademacher variables for the distribution of 푤 such that:

1 with probability 1⁄2 푤 = { Change and Risk −1 with probability 1⁄2 - Issues for Property Valuation As the bootstrapping procedure is computationally intensive, we limit B to work? 1,000 iterations. 8 3.2 Flood-Related Variables

The main variables of interest are drawn from two geographical datasets. The first set is the flood alert map from the UK Environment Agency. The flood map identifies expanses of floodplain that are at risk of low to high impact flooding from main rivers, ordinary watercourses and the sea. We create a dummy variable that equals one for residential properties located inside these flood zones (FLOOD). The second dataset is a collection of water maps that chart the surface water bodies and includes rivers, lakes, estuary and coastal area. That dataset includes Environment Agency’s statutory main river map, the ordinary watercourses map and the maps of waterbodies associated with the Water Framework Directive (WFD). We calculate the distance between each residential property and the nearest waterbody and generate a dummy variable that equals one when a house is deemed near a waterbody (NEARWATER). In our main tests, a property is considered near the water if it is located within 200 feet (60.96m) from a waterbody but we later examine various distance thresholds as robustness checks.

3.3 House Prices and Control Variables

We obtain the data on all transactions of residential properties in the U.K. between 1995 and 2015 from the U.K. Land Registry’s Price Paid Data. The dataset contains over 21 million transactions with information on trade prices, trade dates, and complete street addresses. The land registry distinguishes between four building types, namely detached (D), semi-detached (S), terraced (T) and flat (F) properties, indicates whether or not a transaction involves a new building and differentiates leasehold from freeholds.

Next, we need to geocode each property in order to fit the geographic location of each house within LSOA and to calculate distances to the water bodies and the flood zones. Unfortunately, available geocoding services prevent from geocoding such a large sample of addresses in a reasonable timeframe and force us to keep only a subset of the UK land registry data.

From the U.K. land registry dataset, we identify all postcodes in England that include at least 60 transactions and which contain at least one flood zone. Our final sample contains 100,525 transactions on 51,031 properties spread across 858 postcodes. The geocoding process reveals that the properties are located in 1,220 LSOA. We identify 19,008 transactions that are considered near a waterbody and 21,430 that are located within a flood zone. Figure 1 displays the location of the residential houses included in our final sample as well as the flood zones. We observe that properties are spread across

all England with a higher concentration of retained LSOA located in the large urban agglomeration of Bristol, Liverpool, London and Newcastle.

9

The response variable we use in our study is the trade price expressed in natural logarithm of real £2015 using the county-level Land Registry’s monthly housing price indexed for the adjustment. Using a semi-logarithmic regression model is quite common in empirical economics as it allows interpreting estimated coefficients as the estimated percentage change in Y for a small change in the regressor4.

Most LSOA-level statistics are from the NOMIS web-based database of U.K. labour market statistics and include the population, the population density, a breakdown of the population by economic activity, a breakdown of workers according to their qualification, a breakdown of dwellings by construction type and the number of households by type of holding and with and without mortgages. We also extract general land use statistics from the ONS’ Neighbourhood Statistics Service (NeSS) that gives the proportion of each LSOA area with water, road, buildings or parks, data on the total and average energy (electricity and natural gas) consumption and various indicators related to (un)employment and to the quality of the living environment.

The choice and the shape of the control variables we keep in our final specification are somewhat subjective. We proceed by gradually augmenting the number

4 see Halvorsen and Palmquist HALVORSEN, R. & PALMQUIST, R. 1980. The interpretation of dummy variables in semilogarithmic equations. American economic review, 70, 474-75. or Giles GILES, D. E. 2011. Interpreting dummy variables in semi-logarithmic regression models: Exact distributional results. Department of Economics, University of Victoria, Canada. for discussions on the interpretation of regression’s coefficients in semi-logarithmic models

Change and Risk of regressors in the model using AIC (Ngo and Brand, 2002). At each step we choose the augmented model that minimizes the AIC provided that the model presents no - Issues for significant collinearity and that the incremental regressor is significant at the 0.05 level. Property Valuation work? We use the collinearity test based on the condition index described by Belsley, Kuh and Welsch (2005) to assess collinearity and require that the model present a condition index 10 inferior to 30.

Table 1 presents the 10 control variables we include in our LMM along with the expected sign and summary statistics. We observe that about one third of the transactions involve new buildings. Our sample is slightly titled toward flats and includes less semi-detached houses. The retained LSOA greatly differ with regards to their built environment with some region having no flats and other composed almost exclusively of flats. This situation is also reflected in the density of population which ranges between 0.10 and 328 inhabitants per hectare. The GreenArea variable indicates the proportion of a LSOA that is not covered by buildings and roads. That proportion varies between 22 and 99 percent. The composition of the work force also sharply varies. Some region count more than nine times as many highly educated than lowly educated workers while others have twice as many workers with low level of education than workers with higher education degrees. Finally, we keep in our specification two additional indicators. The index of (un)employment estimates the sum of claimants of sickness related benefits, New Deal participants, and Jobseeker's Allowance claimants expressed as a rate of the relevant population on the whole population aged 18-59 plus men aged 60-64. We believe that this variable is a suitable proxy for area wealth. Our results show that area wealth has a non-linear impact on the flood risk discount and on water-related amenities. Last, the index of poor living conditions combines four indicators to give an overall score for the level of deprivation in the quality of the local environment: the probability that any house in a LSOA will fail to meet 'Decent Homes Standard' as modelled by the Building Research Establishment; the index of overall air quality; the proportion of injuries to pedestrians and cyclists caused by road traffic accidents per 1,000 resident and the percentage of households without central heating. Higher values of IDX_NONEMPL and IX_POORLIVCOND should both be associated with lower house values.

Table 1. Control variables and summary statistics

Regressor Exp sign nobs Min Median Mean Max St.dev. Skew. Kurt. NEW= + 36,804 n.a n.a n.a n.a n.a n.a n.a YES

TYPE= D + 25,040 n.a n.a n.a n.a n.a n.a n.a 11

TYPE= S + 17,313 n.a n.a n.a n.a n.a n.a n.a

HOUSE

-

1 TYPE= T + 26,252 n.a n.a n.a n.a n.a n.a n.a

TYPE= F base 31,920 n.a n.a n.a n.a n.a n.a n.a

% FLATS +/- 100,525 0.00 13.20 21.95 97.86 23.72 1.53 1.54 POP_DEN + 100,525 0.10 28.00 36.37 327.80 38.35 3.21 14.09 SITY RATIO_H + 100,525 0.46 1.26 1.54 9.49 1.10 4.23 23.47

LSOA IGHEDU

-

2 %GREEN + 100,525 22.84 76.76 76.31 99.28 14.00 -0.69 0.86 AREA IDX_NON - 100,525 2.00 7.00 8.64 42.00 5.96 1.64 3.30 EMPL IDX_POO

RLIVCON - 100,525 0.18 13.65 17.98 83.42 15.31 1.28 1.31

D

Adj. Trade

Y n.a 100,525 1,910 181,890 211,000 8,432,600 137,750 7.01 202.39 Price Table 1 presents the control variables included in the linear mixed-effects model along with the expected effect on house prices and summary statistics. NEW is a dummy variable that equals one if the transaction involves a new property. The four dwelling types are detached (D), semi-detached (S), terraced (T) and flats (F). % FLATS represents the proportion of flats on the total number of dwellings in a Lower Layer Super Output Area (LSOA). POP_DENSITY is calculated as the number of residents per hectare. RATIO_HIGHEDU denotes the ratio of workers with higher education qualifications on workers with a lower education degree. %GREENAREA is the proportion of a LSOA area not comprised of buildings, roads or rails. IDX_NONEMPL stand for the rate of out-of-work benefit claimants and is interpreted as a proxy for economic deprivation. IX_POORLIVCOND combined four indicators of the level of deprivation in the quality of the local environment. Adjusted Trade Price is the response variable expressed in real £2015 using the county-level Land Registry’s monthly housing price indexed for the inflation adjustment. KURT indicates the excess kurtosis.

Change and Risk - Issues for 4. Results and Discussion Property Valuation work? 4.1 Main results 12 The analysis of the impact of flood risk on residential property values provides important information on the behaviour of housing market participants. The facts that our study encompassed property geographically diversified across over 1,000 small areas with dissimilar built environments and with various level of economic wealth and that our dataset spans two decades of transactions allow us to obtain more general results that are of interest to a wide array of local and regional governmental authorities as well as to insurance companies active at the regional and country-wide levels. We present our main results in two steps. First, we disclose and interpret the findings related to several specifications of the LMM fitted on the whole sample and discuss the sign and economic significance of the regressors. Second, we test our model on various subsamples built by time periods and by level of economic wealth. Among other things, these tests allow us to observe the differences in behaviours that can originate from the availability of new detailed flood data and/or from the flood awareness programs of the Environment Agency and to observe the confounding effect of economic inequality on flood-relate price discounts (e.g. Penning-Rowsell and Pardoe, 2012). All of our tests use the common set of control variables described in Table 1.

Table 2 shows the effect of proximity to a watercourse and of flood risk on property values using the whole sample according to four distinct model specifications. We observe that all the control variables have the expected sign and obtain similar estimated coefficients across specifications. Average prices for detached houses are greater than that of semi-detached properties which are superior to the average price of terraced houses. Flats obtain the lowest average dwelling value. Newly built properties also trade at higher prices than resale homes. Prices tend to be higher in LSOA with a greater proportion of green areas and are positively correlated with the density of population, the density of dwelling and the proportion of highly skilled workers. As expected, better living environment and upper area wealth also increase average house values.

Table 2. The effect of flood risk and of proximity to the water on property values Regressor MODEL 1 MODEL 2 MODEL 3 MODEL 4 *** *** *** *** INTERCEPT 11.2071 11.2004 11.2086 11.2006 *** *** *** *** NEW= YES 0.0535 0.0535 0.0535 0.0535

*** *** *** *** TYPE= D 0.5884 0.5881 0.588 0.5879 *** *** *** *** 13 TYPE= S 0.3061 0.3062 0.3058 0.3059 TYPE= T 0.2556*** 0.2557 *** 0.2554*** 0.2556 *** *** *** *** *** %GREENAREA 0.0064 0.0064 0.0064 0.0064 *** *** *** *** % FLATS 0.0119 0.0118 0.0119 0.0118 *** *** *** *** IDX_NONEMPL -0.0284 -0.0276 -0.0284 -0.0276 *** *** *** ***

CONTROL VARIABLES IDX_POORLIVCOND -0.0035 -0.0035 -0.0035 -0.0035 *** *** *** *** POP_DENSITY 0.0030 0.0030 0.0030 0.0030 *** *** *** *** RATIO_HIGHEDU 0.0497 0.0499 0.0494 0.0497 *** *** NEARWATER -0.0026 0.0262 0.0354

*** *** NEARWATER:IDX_NONEMPL -0.0037 -0.0037 ** InFLOODZONE -0.0090 -0.0014

FLOODS *** InFLOODZONE:NEARWATER -0.0187 Random effects LSOA:TYPE LSOA:TYPE LSOA:TYPE LSOA:TYPE

Adj_R2 0.8373 0.8373 0.8373 0.8374 Table 2 displays the estimated coefficients of the control and flood-related variables of the linear mixed-effects model with the logarithm of the adjusted trade prices as response variable. Model 1 to 4 represent four distinct model specifications that share the same control variables and random effects but differ with respect to the flood-related variables. The model includes one level of random effects by dwelling type and LSOA. p-values are calculated using the Wild Bootstrap approach of Liu (1988) that take into account heteroscedasticity and departure from the normality in the residuals. *, **, *** indicate statistical significance at the 0.10, 0.05 and 0.01 level, respectively.

Change and Risk The effect of the proximity to the water on housing is more difficult to assess. In model 1, we observe that the NearWater dummy is not statistically significant and - Issues for have an economic meaningless effect of about -0.26 percent on house prices. However, Property Valuation work? the no-impact result clearly originates from the confounding effect of the area wealth. In model 2 we interact the NearWater dummy with our indicator of area wealth and 14 notice that the proximity to a watercourse appears to have a positive effect in wealthier neighbourhood and a negative effect in poorer localities. Both the NearWater and the interaction variables are now highly significant. In model 3 we monitor the impact of being located in a flood zone. As expected, we note that being in a disaster-prone area decreases house values. The effect is statistically significant at a 0.05 level but the economic implications remains somewhat weak as the price of houses located in a flood zone are reduced by less than one percent. Last, model 4 presents the effect of being located in a flood zone while controlling for the proximity to the water. The findings are particularly interesting as they convey convincing indications on the behaviour of the participants in the U.K. housing market. On one hand, being located near a watercourse strongly increase house values in wealthier area of about 3.6 percent. This positive effect of the proximity to the water decreases with economic deprivation and is also much lower for properties set in a flood zone. Surprisingly, being located inside a flood-prone area has almost no impact per se. However, the negative effect of flood risk is obvious if we focus on flood zones situated near a watercourse. The value of these properties is almost 2 percent lower than that of an equivalent property located outside a flood zone and more than 200 feet away from a body of water. Thus, it seems that households consider their dwelling as at risk of flooding only if the source of the flood risk is apparent. We interpret this outcome as a signal that people have an imperfect appreciation of flood risk.

The imperfect assessment of flood risk can stem from several time-related factors such as an average household behaviour characterised by myopia and amnesia, the unavailability of sound data on flood risk at the small area level before 20045, the greater media attention to global warming, or the increased flood awareness that derives from the recent major flood episodes such as the 1998 Easter floods, the 2000 Western Europe floods, the , the 2004 flood or the 2007 U.K. summer floods. The patterns observed in Table 2 can also result from wealth inequality as some areas may be better aware of flood risk and better equipped (through risk mitigation measure or insurance) to face flood episodes.

5 The Environment Agency made a first flood map available on the internet in 2000 but a much more detailed version has been put online in 2004. For more information on UK flood maps, please see http://ec.europa.eu/environment/water/flood_risk/flood_atlas/countries/pdf/uk.pdf

Table 3. The effect of flood risk across subsamples

Panel A - time period Panel B - area wealth 50% 50% Flood-related variable Whole dataset 1995-2003 2004-2015 15 wealthiest poorest 0.0058 0.0097 0.0044 0.0184 -0.0128 NearWater (.099) (.081) (.453) (.001) (.012) 0.0000 0.0177 -0.0147 -0.0141 0.0099 InFloodZone (.480) (.015) (.027) (.040) (.112) -0.0187 -0.0158 -0.0046 -0.0018 -0.0306 InFloodZone:NearWater (.005) (.084) (.593) (.426) (.000) nb obs 100,525 46,487 54,038 48,639 51,886 nb NearWat 19,008 8,231 10,777 7,967 11,041 nb FLOOD 21,430 9,686 11,744 8,910 12,520 Adj_R2 .8373 .8651 .8320 .8062 .8282 Table 3 indicates the estimated coefficients of the flood-related variables. Panel A separates the sample by subperiod. Panel B splits the sample by level of area wealth as proxied by the variable IDX_NONEMPL. nb NearWat sums the number of properties considered as being located near the water and nb FLOOD displays the number of properties located within a flood zone. Wild Bootstrap p-values are in parenthesis.

Change and Risk We investigate the respective merit of these tentative explanations and present the results of that analysis in Table 3. In panel A we segment our sample according to - Issues for the transaction date and choose to distinguish the 1995-2003 from the 2004-2015 periods Property Valuation work? based on flood map availability. We observe that the location inside a flood zone has a much more limited impact before 2004. In fact houses situated far from a body of water 16 and included in a flood zone even benefit from an added value we are unable to explain with our model. That odd premium disappears for properties in a flood-prone area that are also close to a watercourse. Results from 2004-2015 subsample contrast with those of the earlier period. Being located in a flood zone now significantly reduces house value. The negative effect reaches almost two percent for properties in flood zones that are also situated near a body of water. While the size of the effect matches that of the whole sample, it is now the affiliation to a flood zone and not the interaction with water proximity that commands the largest drop in value. This indicates that the average household do incorporate the available information on flood risk in its assessment of a property value. Note that the dummy variable denoting the proximity to the water is not significant at the 0.05 level in both subperiods. This is due to the absence of control for area wealth that acts as a confounding variable.

In panel B of table 3 we separate wealthier from poorer areas on the basis of the indicator of (un)employment IDX_NONEMPL. We notice that the positive and negative effects of the proximity to the water in, respectively, richer and poorer areas are both economically and statistically significant. The effect of flood risk also obviously differs between the two subsamples. In wealthier areas, properties located in a flood zone exhibit a lower value that is significant at the 0.05 level. The proximity to the water slightly and insignificantly worsens house values. Contrasting with richer areas, households in more economically deprived areas appear to pay less attention to flood zones per se. Hence, poorer households seem to automatically associate the proximity to the water with flood risk. Properties near a body of water in poorer localities are worth about 1.3 percent less than comparable houses located elsewhere. If the property is also located in a flood zone, the combined negative effect reaches 4.3 percent. Thus, richer and poorer households exhibit distinct behaviours.

In terms of public policy, the U.K. Environment Agency prioritised the need to increase public flood risk awareness in the mist of the large floods of 1998 and 2000. Despite several criticisms addressed to the Agency in recent years, our results indicate a clear improvement in risk awareness, at least among categories of households. Housing market participants in richer localities appear to charge a risk premium for property in flood-prone areas since 2004 whereas no flood-related effect is observable before that period. In poorer areas, flood risk awareness also progresses but households appear to rely essentially on visible threats to assess flood risk. These dichotomous evidences can somewhat be related to the more general issue of financial illiteracy (Atkinson et al., 2007, Lusardi and Mitchell, 2011).

4.2 Robustness tests

We implement several additional analyses to benchmark our main results. First, we augment our set of control variables by adding the 2011 rural-urban classification for LSOA from the Department for Communities and Local Government and by including a dummy variable that equals one for leasehold estate. We notice that 17 many rural classifications are not statistically significant, perhaps because of a relatively small number of observations in some classes. As expected, properties in urban localities tend to show higher prices while houses in rural LSOA display lower prices. Also, leasehold houses have significantly lower values than freehold properties. All in all, flood-related variables yield similar signs and significance and thus fully support our main results.

Second, we truncate our sample to exclude the transactions within the 0.5 percent higher and 0.5 percent lower quantiles of the distribution of adjusted trade prices. The truncation reduces the amplitude of adjusted house prices from £1,910 – £8,432,600 as display in table 1 to £ 38,520 – £991,347 (in real £2015). The exclusion of the most extreme data slightly increases the model’s adjusted R-squared but have no material effect on the coefficients of the control variables or on the coefficients of the flood-related variables.

Last, we investigate the effect of varying the distance to the nearest body of water used to considered a property as being near a watercourse. We used a 200-feet distance in our base specification and now test various distances ranging from 100 to 750 feet. Table 4 shows the results of that analysis. We observe that the added value of being near the water is not limited to the very banks of a river. Water-related amenities even increase up to a 400 feet from a watercourse before losing significance at greater distance. The impact of being located in a flood zone is concentrated in the immediate vicinity of the waterbodies. The negative economic impact exceeds 2.5 percent for properties in floodplains less than 100 or 150 feet away from the water and drops of about 1.5 percent when the distance threshold is greater or equal to 250 feet. Thus, our main results are qualitatively robust to the distance used to consider a property as being near the water.

Change and Risk Table 4. The effect of varying the distance threshold for being near a watercourse - Issues for Distance for being near a watercourse (in feet) Property Valuation Flood-related variable 100 150 200 250 400 500 750 work? 0.0145 0.0354 0.0354 0.0326 0.0439 0.0417 0.0097 NearWater 18 (.061) (.000) (.000) (.000) (.000) (.000) (.049) -0.0019 -0.0031 -0.0037 -0.0036 -0.0038 -0.0038 -0.0002 NearWater:IDX_NONEMPL (.003) (.000) (.000) (.000) (.000) (.000) (.710) 0.0008 -0.0020 -0.0014 -0.0046 -0.0057 -0.0022 0.0095 InFloodZone (.860) (.671) (.779) (.392) (.319) (.717) (.183) -0.0267 -0.0239 -0.0187 -0.0108 -0.0099 -0.0136 -0.0233 InFloodZone:NearWater (.000) (.000) (.004) (.095) (.125) (.039) (.000) nb NearWat 10,566 15,279 19,008 22,574 34,404 43,701 64,365 nb FLOOD 21,430 21,430 21,430 21,430 21,430 21,430 21,430 nb FLOOD&NearWat 6,338 8,248 9,742 10,803 12,922 15,113 17,792 Table 4 presents the estimated coefficients of the flood-related variables according to various distance thresholds to consider a property as being located near to the water. nb NearWat sums the number of properties considered as being located near the water, nb FLOOD displays the number of properties located within a flood zone and nb FLOOD&NearWat discloses the number of properties located within a flood zone that are also near the water. Wild Bootstrap p-values are in parenthesis.

5. Conclusion

Previous papers have examined the impact of recent floods, or of a location in a flood-prone area, on property values. However, most studies focus on a relatively small geographic region which often prevents from drawing general conclusions on the countrywide housing market. Fielding (2007) finds that economically deprived 19 households tend to be overrepresented in flood areas. As a result, studies focussing on richer (poorer) areas may underestimate (overestimate) the effect of flood risk on house prices. Furthermore, Daniel et al. (2009) argue that the very proximity to a body of water acts as a potential confounding effect and that many studies fail to adequately control for that effect.

This paper explores the consequences of flood risk on property prices and alleviates the preceding concerns. We use a sample of England properties totalling over 100,000 transactions that are spread across all the country. Our database spans rural and urban localities as well as richer and poorer small regions. We calculate distances between each properties and areas of interests using geographical-based datasets that map hundreds of bodies of water and of flood zones. Consequently, we are able to control for wealth inequality, proximity to the water and various other important determinant of housing to test the impact of flood risk. We employ a linear mixed-effects model to take into account the local housing market dynamic that affects house prices and correct for heteroscedasticity or for potential departure from normality in residuals using a wild bootstrap methodology.

Our results bring additional evidences that are consistent with most previous theoretical and empirical findings. We observe that houses being located inside flood zones are worth less than equivalent properties located elsewhere. The difference in value is of about two percent which is both economically and statistically significant. As expected, we notice that proximity to the water acts as a confounding factor, but only in wealthier areas. In those areas, the added value of being located near a watercourse exceeds the negative impact of being in a flood zone. In more economically deprived areas, both proximity to the water and inclusion in a flood zone yield a negative coefficient. In addition, different level of wealth seems to be associated with dissimilar household behaviours. People in richer areas appear to better incorporate available flood risk data as they require a price discount for all properties that are located in a flood zone. This contrasts with the situation in poorer areas where we observe a drop in value only for the properties at risk near a body of water. Thus poorer households appear to assess flood risk on the basis of visible threats. Such an interpretation can be related to the findings of Burningham, Fielding and Thrush (2008) who conclude that the problem is often not simply a lack of awareness, but an assessment of local risk based on flood experience that underestimate the likely consequences of floods.

Given that information on flood risk for small geographic regions is only recently available and that incorporating the effect of new flood data in prices takes time, we examine whether or not household behaviour differ between before and after detailed flood-related data is easily available. We observe an obvious difference between

Change and Risk the two subperiods in that the negative effect of flood risk is only perceptible in the post- 2003 period. - Issues for

Property Valuation work? Finally, our findings suggest that the major flood awareness campaigns put forward by the Environment Agency in the wake of the 1998 and 2000 major flood 20 episodes may have contributed to modify the behaviour of housing market participants that now better include flood risk in real estate assessment, at least in wealthier areas. Yet work still need to be done in more economically deprived areas where households tend to rely on visible threats to assess flood risk and future flood awareness campaigns would particularly benefit from targeting lower-income localities. Our results may be of interest to help define policies regarding conflicts arising from foreseeable future revisions in flood zone boundaries or regarding the estimation of the value added by the implementation of flood mitigation measures.

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