Spatial Dependence and House Price Index Construction

Accurate estimation of prevailing metropolitan housing prices is important for both business and research investigations of housing and mortgage markets. This is typically done by constructing quality-adjusted house price indices from hedonic price regressions for given metropolitan areas. A major limitation of currently available indices is their insensitivity to the geographic location of dwellings within the metropolitan area. Indices are constructed based on models that do not incorporate the underlying spatial structure in housing data sets. In this article, we argue that spatial structure, especially spatial dependence latent in housing data sets, will affect the precision and accuracy of resulting price estimates. We illustrate the importance of spatial dependence in both the specification and estimation of hedonic price models. Assessments are made on the importance of spatial dependence both on parameter estimates and on the accuracy of resulting indices.     

Anisotropic Autocorrelation in House Prices

This article examines anisotropic spatial autocorrelation in single-family house prices and in hedonic house-price equation residuals using a spherical semivariogram and transactions data for one county in the Philadelphia, Pennsylvania, MSA. Isotropic semivariograms model spatial relationships as a function of the distance separating properties in space. Anisotropic semivariograms model spatial relationships as a function of both the distance and the direction separating observations in space. The goals of this article are (1) to determine whether there is spatial autocorrelation in hedonic house-price equation residuals and (2) to empirically examine the validity of the isotropy assumption. We estimate the parameters of spherical semivariograms for house prices and for hedonic house-price equation residuals for 21 housing submarkets within Montgomery County, Pennsylvania. These housing submarkets are constructed by dividing the county into 21 groupings of economically similar adjacent census tracts. Census tracts are grouped according to 1990 census tract median house prices and according to characteristics of the housing stock. We fit the residuals of each submarket hedonic house price equation to both isotropic and anisotropic spherical semivariograms. We find evidence of spatial autocorrelation in the hedonic residuals in spite of a very elaborate hedonic specification. Additionally, we have determined that, in some submarkets, the spatial autocorrelation in the hedonic residuals is anisotropic rather than isotropic. The empirical results suggest that the spatial autocorrelation in Montgomery County single-family house-price equation residuals is anisotropic in submarkets where residents typically commute to a regional or local central business district.     

The Hierarchical Trend Model for Property Valuation and Local Price Indices

This paper presents a hierarchical trend model (HTM) for selling prices of houses, addressing three main problems: the spatial and temporal dependence of selling prices and the dependency of price index changes on housing quality. In this model the general price trend, cluster-level price trends, and specific characteristics play a role. Every cluster, a combination of district and house type, has its own price development. The HTM is used for property valuation and for determining local price indices. Two applications are provided, one for the Breda region, and one for the Amsterdam region, lying respectively south and north in The Netherlands. For houses in these regions the accuracy of the valuation results are presented together with the price index results. Price indices based on the HTM are compared to a standard hedonic index and an index based on weighted median selling prices published by national brokerage organization. It is shown that, especially for small housing market segments the HTM produces price indices which are more accurate, detailed, and up-to-date.     

A Spatial Autocorrelation Approach for Examining the Effects of Urban Greenspace on Residential Property Values

This paper presents spatially explicit analyses of the greenspace contribution to residential property values in a hedonic model. The paper utilizes data from the housing market near downtown Los Angeles. We first used a standard hedonic model to estimate greenspace effects. Because the residuals were spatially autocorrelated, we implemented a spatial lag model as indicated by specification tests. Our results show that neighborhood greenspace at the immediate vicinity of houses has a significant impact on house prices even after controlling for spatial autocorrelation. The different estimation results from non-spatial and spatial models provide useful bounds for the greenspace effect. Greening of inner city areas may provide a valuable policy instrument for elevating depressed housing markets in those areas.     

Property-Value Impacts of an Environmental Disamenity: The Case of Landfills

The purpose of this article is to quantify the property-value impacts of a change in environmental quality by using the hedonic price model. In particular, we focus on the impact of the presence of landfills on nearby residential real estate prices. We combine elements of an urban location choice and hedonic pricing model to estimate the effects of the presence of multiple environmental disamenities on residential real estate prices. We explicitly account for temporal effects by including housing transactions in areas with both open and closed landfills and control for information effects. In addition, we treat property taxes and prices as being simultaneously determined. Our analysis suggests that closing landfills will not necessarily mitigate property-value impacts.     

Nonlinear Spatial and Temporal Effects of Highway Construction on House Prices

This paper studies the effect of a newly completed highway extension on home prices in the surrounding area. We analyze non-linearities in both the effect of distance from the highway and the effect of time relative to the completion of the road segment. While previous studies of the effects of nearby amenities on property and land values have focused on either cross-sectional spatial or temporal patterns, the joint analysis of the two dimensions has not been thoroughly investigated. We use home sale data from a period of 11 years centered around the completion of a new highway extension in metropolitan Los Angeles. We combine a standard hedonic model with a spline regression technique to allow for non-linear variations of the effect along the temporal and spatial dimensions. Our empirical results show that the maximum home price appreciation caused by the new highway extension occurs at moderate distances from the highway after it is completed. Lower price increases for this period are observed for homes sold closer to the highway or much further away. This price pattern gradually fades away in the years following the construction completion. A similar, although weaker, price pattern is also observed in the first years of the construction period. There is no statistically significant distance dependency in the 2 years in our sample prior to the beginning of the construction. This indicates that the housing market is not fully efficient as the information about the impending construction of the highway is not immediately incorporated into sales prices.     

Spatial Dependence,Housing Submarkets,and House Price Prediction

This paper compares alternative methods of controlling for the spatial dependence of house prices in a mass appraisal context. Explicit modeling of the error structure is characterized as a relatively fluid approach to defining housing submarkets. This approach allows the relevant submarket to vary from house to house and for transactions involving other dwellings in each submarket to have varying impacts depending on distance. We conclude that—for our Auckland, New Zealand, data—the gains in accuracy from including submarket variables in an ordinary least squares specification are greater than any benefits from using geostatistical or lattice methods. This conclusion is of practical importance, as a hedonic model with submarket dummy variables is substantially easier to implement than spatial statistical methods.

Spatiotemporal Autoregressive Models of Neighborhood Effects

Using 70,822 observations on housing prices from 1969 to 1991 from Fairfax County Virginia, this article demonstrates the substantial benefits obtained by modeling the spatial as well as the temporal dependence of the data. Specifically, the spatiotemporal autoregression with twelve variables reduced median absolute error by 37.35% relative to an indicator-based model with twenty-six variables. One-step ahead forecasts also document the improved performance of the proposed spatiotemporal model. In addition, the article illustrates techniques for rapidly computing the estimates and shows how to compute indices for any location.     

Spatial Variation in the Determinants of House Prices and Apartment Rents in China

This paper provides an examination of China??s residential real estate market at the county level using data from that country??s 2000 census. The market is a new one, having only been fully established in 1998. The analysis in the paper is in the form of an aggregate (county-level) hedonic model specified in two versions. Global parameters results are estimated using spatial error model specifications while more local effects are estimated by geographically weighted regression. Global results are typical in that structural characteristics such as floor space and contextual characteristics such as level of in-migration are important in residential prices. Local results, however, indicate significant spatial variation in the effect of both structural amenities and locational context on housing prices. In a simpler specification, rents are shown to respond positively to both median house prices levels and the supply of apartments available at market prices, but also with significant spatial variation across China.     

Analysis of Spatial Autocorrelation in House Prices

This article examines spatial autocorrelation in transaction prices of single-family properties in Dallas, Texas. The empirical analysis is conducted using a semilog hedonic house price equation and a spherical autocorrelation function with data for over 5000 transactions of homes sold between 1991:4 and 1993:1. Properties are geocoded and assigned to separate housing submarkets within metropolitan Dallas. Hedonic and spherical autocorrelation parameters are estimated separately for each submarket using estimated generalized least squares (EGLS). We find strong evidence of spatial autocorrelation in transaction prices within submarkets. Results for spatially autocorrelated residuals are mixed. In four of eight submarkets, there is evidence of spatial autocorrelation in the hedonic residuals for single-family properties located within a 1200 meter radius. In two submarkets, the hedonic residuals are spatially autocorrelated throughout the submarket, while the hedonic residuals are spatially uncorrelated in the remaining two submarkets. Finally, we compare OLS and kriged EGLS predicted values for properties sold during 1993:1. Kriged EGLS predictions are more accurate than OLS in six of eight submarkets, while OLS has smaller prediction errors in submarkets where the residuals are spatially uncorrelated and the estimated semivariogram has a large variance.     

The relationship between rents and prices of owner-occupied housing in Taiwan

This article analyzes the changes of equilibrium rent and equilibrium price of owner-occupied housing in Taiwan, and also computes the rent multiplier and its trend in the past ten years in Taiwan to show how the housing consumption and housing investment change. A hedonic rent equation and a hedonic housing price equation are built first. Then, we apply the Housing Survey Report data from 1979 to 1989, and employ ordinary-least squares method to estimate the two equations. Using estimated coefficients of the two equations, we compute the market rents for owner-occupied housing and the market prices for rental housing. Finally, the rent multipliers are calculated from the market rents and market prices. The article finds that (1) changes of housing prices in Taipei lead to price changes in Kaoshung, and the latter leads Taiwan province; (2) changes of rent are much smaller than the changes of housing price; and (3) housing prices in Taiwan increased drastically. We also find: (1) at the peak of the housing market cycle, the rent multiplier is extremely high; (2) the rent multiplier drops in the year after the peak year because the rent catches up; (3) the rent multiplier in Taipei is greater than that of Kaoshung, and the multiplier in Kaoshung is greater than that of Taiwan province; and (4) overall, the rent multiplier in Taiwan is much greater than that of the United States.     

Spatial Autocorrelations and Urban Housing Market Segmentation

This paper seeks to let data define urban housing market segments, replacing the conventional administrative or any pre-defined boundaries used in the previous housing submarket literature. We model housing transaction data using a conventional hedonic function. The hedonic residuals are used to estimate an isotropic semi-variogram, from which residual variance–covariance matrix is constructed. The correlations between hedonic residuals are used as identifier to assign housing units into clusters. Standard submarket identification tests are applied to each cluster to examine the segmentation of housing market. The results are compared with the prevailing structure of market segments. Weighted mean square test shows that the defined submarket structure can improve the precision of price prediction by 17.5%. This paper is experimental in the sense that it represents one of the first attempts at investigating market segmentation through house price spatial autocorrelations.     

The Construction of Residential Housing Price Indices: A Comparison of Repeat-Sales,Hedonic-Regression,and Hybrid Approaches

This article examines a number of hypotheses that underpin the repeat-sales and hedonic approaches to the construction of housing price indices, as well as the practical problems associated with the implementation of either approach. We also examine a hybrid procedure that combines elements of both the repeat-sales and hedonic-regression techniques. For our sample of individual home sales in Oakland and Fremont California over an 18-year period, repeat-sales methods are subject to sample selection bias; the maintained assumption of time constancy of implicit prices of housing attributes is violated; the repeat-sales estimator is extremely sensitive to influential observations; and the usual method used to correct for heteroskedasticity in repeat-sale housing returns is inappropriate in our sample. Hedonic techniques are better suited to contend with index number problems per se, as they can accommodate changing attribute prices over time. They also appear to give rise to more reliable estimates of price indices, as unusual observations have less effect on estimated price indices. Drawbacks of the hedonic approach include the usual concern with omitted attributes, and their effect on the estimated price index.     

Housing Price Dynamics in Time and Space: Predictability,Liquidity and Investor Returns

It is widely accepted that aggregate housing prices are predictable, but that excess returns to investors are precluded by the transactions costs of buying and selling property. We examine this issue using a unique data set—all private condominium transactions in Singapore during an eleven-year period. We model directly the price discovery process for individual dwellings. Our empirical results clearly reject a random walk in prices, supporting mean reversion in housing prices and diffusion of innovations over space. We find that, when house prices and aggregate returns are computed from models that erroneously assume a random walk and spatial independence, they are strongly autocorrelated. However, when they are calculated from the appropriate model, predictability in prices and in investment returns is completely absent. We show that this is due to the illiquid nature of housing transactions. We also conduct extensive simulations, over different time horizons and with different investment rules, testing whether better information on housing price dynamics leads to superior investment performance.     

经济集聚、人口流动与住宅价格空间溢出效应研究

基于省级面板数据,利用空间计量模型考量经济集聚、人口流动和住宅价格间的空间溢出效应关系.结果发现:中国省际住宅价格间存在着显著的正向空间相关性,即某一地区住宅价格的波动会受到周边地区或经济特征类似地区住宅价格波动的影响;人口流动在经济集聚和房价的关系中发挥着中介作用;经济集聚与住宅价格在空间上存在着显著的相关关系,经济集聚不仅会推高集聚中心住宅价格,还会通过"虹吸效应"和"涓滴效应"对周边住宅价格产生影响,而经济集聚对周边住宅价格的影响取决于人口净流入率的大小.     

The Choice of Methodology for Computing Housing Price Indexes: Comparisons of Temporal Aggregation and Sample Definition

Housing transactions are executed and recorded daily, but are routinely pooled into longer time periods for the measurement and analysis of housing price trends. We utilize an unusually rich data set, covering essentially all arm's length housing sales in Sweden for a dozen years, in an attempt to understand the effect of temporal aggregation upon estimates of housing prices and their volatilities. This rich data set also provides a unique opportunity to compare the results using the conventional weighted repeat sales model (WRS) to those based on a research strategy which incorporates all available information on house sales. The results indicate the clear importance of temporal disaggregation in the estimation of housing prices and volatilities—regardless of the model employed.The appropriately disaggregated model is then used as a benchmark to compare estimates of the course of housing prices produced by the two models during the twelve year period 1981–1993. These results indicate that much of the difference between estimates of price movements can be attributed to the data limitations which are inherent in the repeat sales approach. The results, thus, suggest caution in the interpretation of government-produced price indices or those produced by private firms based on the repeated sales model.     

A Dynamic Model of the Housing Market: The Role of Vacancies

While the hedonic property value model and recently developed computable general equilibrium urban models assume the housing market is in equilibrium, recent years have witnessed extreme circumstances such as large changes in housing prices, high levels of mortgage default, and high levels of foreclosure that bring into question this assumption. This highlights the need for a better understanding of the dynamics of the housing market and the mechanisms that drive and sustain periods of disequilibrium. In this analysis, I develop a dynamic model of the housing market where vacancies naturally arise as the error correction mechanism. I estimate this model using annual U.S. panel data at the MSA level for 1990–2011. The results show that when there is excess demand, prices rise when vacancies fall but prices do not fall when there is excess supply and vacancies rise. This is consistent with the belief that prices are sticky downwards and hence prolong housing downturns. On the other hand, when there is excess supply, there is a relatively stronger decline in new housing in response to a rise in vacancies and much less of a new housing reaction when there is excess demand and vacancies fall. Furthermore, when I allow for a structural shift in the housing market brought on by the Great Recession (2006–2011), I find that the housing market became more responsive on both sides – excess supply and demand – during this period.     

Models for Spatially Dependent Missing Data

Most hedonic pricing studies using transaction data employ only sold properties. Since the properties sold during any year or even decade represent only a fraction of all properties, this approach ignores the potentially valuable information content of unsold properties which have known characteristics. In fact, explanatory variable information on house characteristics for all properties, sold and unsold, are often available from assessors. We set forth an estimation approach that predicts missing values of the dependent variable when the sample data exhibit spatial dependence. Employing information on the housing characteristics of both sold and unsold properties can improve prediction, increase estimation efficiency for the missing-at-random case, and reduce self-selection bias in the non-missing-at-random case. We demonstrate these advantages with a Monte Carlo experiment as well as with actual housing data.     

Residential Construction Costs and the Supply of New Housing: Endogeneity and Bias in Construction Cost Indexes

Change in the level of residential construction affects macroeconomic conditions and is an important determinant of movements in house prices. Theory teaches us that increases in the cost of construction should reduce the supply of new housing. Yet empirical research has failed to find a consistent relationship between these costs and housing starts. This article introduces an entirely new set of micro-data on housing construction costs to study this issue. We develop quality-controlled, hedonic construction cost series from these data. Using this series, we estimate housing supply and construction cost functions for new single-family residences. This research demonstrates that bias in the commercial cost indexes used in existing housing supply studies is a likely cause of their poor performance in existing estimates of the supply of new single-family housing. The bias appears to be caused by an incorrect measure of labor costs and a failure to address the endogeneity of construction costs and construction activity. In contrast, starts regressions using the hedonic cost series generate much more sensible results. We find that housing starts are quite cost elastic; construction costs are endogenous in the new housing supply function, and the cost shares of material and labor in the structure of new residences are approximately 65 and 35%, respectively.     

The Interaction between Mortgage Financing and Housing Prices in Greece

Although the close empirical relationship between the evolution of mortgage lending and housing prices is well established in the literature, the direction of causation is less clear from a theoretical standpoint. We apply multivariate cointegration techniques in order to address this issue empirically for the Greek economy. Our results, based on a cointegration relationship that we identify as a mortgage loan demand equation, indicate that housing prices do not adjust to disequilibria in the market for housing loans. This suggests that in the long run the causation does not run from mortgage lending to housing prices. In the short run we find evidence of a contemporaneous bi-directional dependence.