Self-Reported Dwelling Valuations—How Accurate Are They? Dmitri Romanov Larisa Fleishman Aviad Tur-Sinai Israel Central Bureau of Statistics

There are three kinds of estimates for a dwelling value: A. Price agreed upon in a sale transaction B. Appraiser's valuation C. Owner’s self-reported valuation elicited by surveys with a question: “What do you think your home is worth? That is, what do you think you could get for your home if you sold it now?” “What do you think your home is worth? That is, what do you think you could get for your home if you sold it now?”

Uses of self-reported dwelling valuations In calculating housing price index In research of the housing market and household’s economic behavior In micro-economic empirical analysis, as an indicator of household’s economic status and well-being There is an immense importance to test the extent of accuracy of subjective dwelling valuations, the differences between these valuations and the benchmarks (appraiser’ estimates or sale prices) and the factors associated with these differences

Summary of previous research The most referred studies that focused on investigation of accuracy of subjective dwelling valuations: Kain & Quigley (1972), Robin & West (1977), Follain & Malpezzi (1981), Goodman & Ittner (1992); Kiel & Zabel (1999) overestimate On average, owners tend to overestimate the value of their homes by around 5%, though the estimates of the bias range from minus 2 percent to 16 percent The extent of overestimation is not correlated with owner, property or neighborhood characteristics Thus, the surveys do provide reasonable estimates of dwelling valuations (with a uniform slight upward bias)

Value-added of this study Focus on investigating the accuracy of subjective valuations across a distribution of dwelling values Focus on investigating the accuracy of subjective valuations across a distribution of dwelling values Based on a large dataset (more then 21K observations) that contains a rich variety of homeowner, dwelling, neighborhood and environment characteristics and covers more than a decade (1997–2008) Based on a large dataset (more then 21K observations) that contains a rich variety of homeowner, dwelling, neighborhood and environment characteristics and covers more than a decade (1997–2008) Examine the interval of time in which “news” about transaction prices in the respondents’ residential environment “trickle-down” into individuals’ dwelling evaluations Examine the interval of time in which “news” about transaction prices in the respondents’ residential environment “trickle-down” into individuals’ dwelling evaluations

This study aims: To examine the extent of accuracy of homeowners’ subjective valuations across a distribution of dwelling sale prices, while exploring: To examine the extent of accuracy of homeowners’ subjective valuations across a distribution of dwelling sale prices, while exploring: whether the valuations of inexpensive and expensive properties are biased in the same direction;  whether the valuations of inexpensive and expensive properties are biased in the same direction;  whether the variance of the bias is homoscedastic across the distribution of property value  what factors are associated with the valuation bias

Data construction Household Expenditure Surveys Annual sample of more than 6000 households countrywide. Pooled sample of 72K in 12 years Sale Transactions Files 612K transactions in Research Dataset Owners’ dwelling valuations Demographic, social, economic indicators of the household and its head Characteristics of the property of the property Data on demographic and socioeconomic indicators of population by census tract Prices, timing and number of transactions by census tract Population Register & Income Tax records GIS Data on environment Data on environment and location indicators of neighborhood

Research population Households: that live in dwellings they own (71% of the survey sample); that live in dwellings they own (71% of the survey sample); whose dwellings were geo-referenced at the level of structure or census tract; whose dwellings were geo-referenced at the level of structure or census tract; that provided valuation of their dwellings (less of 15% of item non-response); that provided valuation of their dwellings (less of 15% of item non-response); that live in Israel’s sixty largest cities (assumed of having regular housing market conditions) that live in Israel’s sixty largest cities (assumed of having regular housing market conditions) After merging the records on these households with an average price of transactions in the census tract in the three-months window preceding the survey interview, working file contained 21,238 observations

Correlation between subjective valuation and average price of sales transactions in census tract, by deciles of valuation and period of time between valuation and sales transactions

Subjective valuation vs. average dwelling price in census tracts, by percentiles of dwelling-price distribution, 1997–2008

Average bias of subjective valuation and its standard deviation

Average bias in subjective valuation by selected percentiles of annual dwelling-price distribution, 1997–2008

Average price of dwelling, by selected percentiles of annual dwelling-price distribution, 1997–2008 (in current prices, index 1997=100)

Main results (1) Homeowners tend to overestimate the value of their dwellings by 27 percent, with the median bias of 23 percent The valuations of inexpensive and costly dwellings are biased in different directions: estimates reported by people who live in the dwellings that are placed in the first eight deciles of the price distribution are upward- biased, whereas those who live in the most expensive dwellings tend to understate the value of their homes In relative terms, subjective valuations bias is much higher among the owners of inexpensive dwellings Variance of valuation bias is heteroscedastic in the lowest 20 and the top 10 percentiles of the price distribution

The model Dependent variable Dependent variable: bias in subjective dwelling valuation relative to the average sale-transaction price of dwellings in the census tract in the three months preceding the survey ’ ‘Household’ - indicators of an owner-occupier household (number of persons, average income per capita in household, and size of the mortgage loan) ‘’ ‘Personal’ - homeowner characteristics (sex, age, marital status, origin etc.) ‘ ‘Asset’- dwelling indicators (whether it is a stand-alone house or an apartment in a condominium building, number of rooms, age of building etc.) ‘ ‘AvgTract’ - indicators of the census tract in which the dwelling is located (socio- demographic characteristics of residents) ‘Area’ – environmental characteristics of the dwelling’s nearest surroundings ‘dUSD’ - the change in the exchange rate (NIS/dollar) in the three months preceding the survey date ’ ‘NumTransactCT’ - the number of sales transactions in the census tract three months preceding the individual’s subjective valuation ‘ ‘Year’ - fixed effect for the year in which the subjective valuation of dwelling i was given ‘ ‘CT’ - fixed effect for census tract in which the valuated dwelling i was located

Models estimated in the study Subjective valuation, a hedonic price model with addition of average price and the number of transactions in census tract Subjective valuation, a hedonic price model with addition of average price and the number of transactions in census tract 21,238  On the full sample (21,238 obs.)  On the sub-sample of dwellings purchased in 12 months preceding the survey (738 obs.) Bias in subjective dwelling valuation, according to the main model: Bias in subjective dwelling valuation, according to the main model: (A)  For the full sample with a fixed effect for year (A) (B)  For the full sample with a fixed effect for census tract (B) (C)  For the sub-sample of dwellings purchased in 12 months preceding survey (C)  For the sub-samples of census tracts in which no fewer than ten, twenty and thirty observations were found in a census tract in the whole research period

Bias model estimates Full sample Sample of dwellings purchased in 12 months preceding survey (C) Fixed effect for years (A) Fixed effect for census tract (B) Variables Estimate (Std. error) Persons 0.010* (0.002)0.009* (0.002)0.023*** (0.012) Income 0.066* (0.005)0.067* (0.005)0.102* (0.027) Mortgage-0.004* (0.001)-0.003* (0.001)-0.008*** (0.004) Age0.004* (0.001)0.003** (0.001)0.002 (0.005) AgeSq ** ( ) *** ( ) ( ) IsraelBorn0.025* (0.008)0.020** (0.008) (0.049) Immigrant * (0.011)-0.052* (0.010) (0.050) ImmEurAmer *** (0.009) (0.008) (0.055) Married 0.041* (0.008)0.045* (0.007)0.044 (0.034) * - Significant at.01 level ** - Significant at.05 level *** - Significant at.10 level

Full sample Sample of dwellings purchased in 12 months preceding survey (C) Fixed effect for years (A) Fixed effect for census tract (B) Variables Estimate (Std. error) Purchased * (0.011)-0.105* (0.010) (0.080) House 0.109* (0.007)0.143* (0.007)0.122* (0.030) Rooms 0.172* (0.003)0.196* (0.003)0.131* (0.017) BuildingAge * (0.001)-0.014* (0.0007) (0.003) BuildingAgeSq * ( ) *** ( ) AirConditioner 0.046* (0.007)0.050* (0.007) (0.029) Heating 0.056* (0.010)0.064* (0.011) (0.056) Garden 0.072* (0.019)0.114* (0.018) (0.064) Bought within 12 months 0.027*** (0.015)0.002 (0.014)- Price * (0.030) dUSD 0.751* (0.076)0.712* (0.073)0.133 (0.335) NumTrasactCT * (0.001)-0.006* (0.001)-0.005** (0.002) MaleCT * (0.177)0.189 (0.249) (0.873)

Full sample Sample of dwellings purchased in 12 months preceding survey (C) Fixed effect for years (A) Fixed effect for census tract (B) Variables Estimate (Std. error) AgeCT 0.008* (0.001)0.002 (0.001)0.003 (0.004) Immigrant1990CT 0.285* (0.029)0.217** (0.088)0.299** (0.139) ImmEurAmerCT * (0.064) (0.137) (0.313) ImmAsiaAfricaCT * (0.071) (0.149) (0.326) EthiopiaCT 0.738* (0.103)0.169 (0.210)1.320* (0.442) IncomeCT * (0.007)0.002 (0.015)-0.131* (0.033) RoadArea 0.002** (0.001) (0.001)0.006 (0.004) Road5mArea ** (0.004)0.003 (0.004) (0.017) SchoolArea 0.003* (0.001) (0.002)0.002 (0.003) No’ of Obs. 21, Mean dependent variable Mean bias (pct.) Adjusted R

Main results (2) The interval of three months before the survey date is found to have the highest correlation between valuations and average transaction prices in a given census tract for most of the dwelling-value distribution No dependency between the extent of valuation bias and sample size in the census tract There is a significant and systematic relation between subjective valuation bias and homeowner personal indicators, dwelling properties and census-tract population characteristics Basic dwelling characteristics (number of rooms and age of building) and the socioeconomic level of the neighborhood exert a larger impact on valuation bias among the owners who bought their dwellings during the twelve months preceding the survey date, relatively to the rest of owners, whereas the bias among the latter is more influenced by dwelling facilities (air conditioning, heating systems, garden)

The observation of a mean (upward) bias in subjective valuations relative to actual dwelling prices masks quite a bit of variation across the distribution; the bias may even change the sign along the distribution. What the reasonable explanations could be assumed? The bottom line

? What explains the bias sign change? Does it square with luxury housing as a Veblen good? Is it about a segment of housing market where subjective valuations do not catch up systematically with a pace of prices rise? Do owners of expensive dwellings differ from the owners of lower-priced accommodations in the manner of thinking and the considerations that figure in evaluating a dwelling? Does an upward bias at the lower end of the dwelling- valuation distribution take shape as a consequence of liquidity constraints among owners of inexpensive dwellings that bring about a price level for such dwellings under fair market value? Is an overvaluation of the least expensive dwellings by their owners akin to an extremely importance of certain utilities of home ownership (stability, local welfare services, community support etc.) for low-income persons?

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