An Evaluation of Hedonic Price Indexes for Thirty-four Large SMSAs

The purpose of this paper is twofold. First, a set of price indexes are developed for thirty-four metropolitan areas for the years 1975 and 1978. Indexes are also broken down by central city and suburb for twenty two of the metropolitan areas. These indexes measure variation in the price per unit of owner-occupied housing stock and the price per unit of housing services of rental housing. Confidence intervals are constructed for the price indexes. It is found that the indexes are reasonably precise measures in that the typical ratio of the confidence interval to the predicted median rent or value is about 13%. Also, analysis of the confidence intervals indicates substantial and statistically significant variation in the price of housing stock and services among the metropolitan areas studied. One of the major benefits of this set of indexes is that it can be used to address one of the most important questions in real estate — Why do housing prices and rents vary among metropolitan areas and over time? Research projects are currently underway that use the data to address these two questions.     

The Asset Approach to Pricing Urban Land: Empirical Evidence

Many papers have attempted to explain Intelmetropolitan variations in the price of housing using multi-equation models of the metropolitan housing market. This paper uses a long-run equilibrium urban asset model to explain such variations. The model builds upon previous models that introduce uncertainty into the dynamic urban model of land conversion. The empirical results strongly support the asset approach to valuing land in urban areas.     

Urban Housing Markets Modelling: Short-Run Equilibrium Implications

This paper tests for the existence of short-run equilibrium in the urban housing market in Metropolitan Toronto. The alternative hypothesis is the housing market segmented with respect to locational and structural attributes. We found insignificant differences in attribute prices across hypothesized submarkets. This implies that an unstratified hedonic price regressions model, based on the assumption of short-run equilibrium, is equally efficient in the analysis of housing prices as a model based on a number of subsamples stratified along lines of segmentation.     

Inflation and Housing Prices*

Home ownership is a claim on the stream of net rents. Like any income-producing asset, the market capitalizes its value. The price-rent multiple depends upon the expected growth rate of revenues and expenses, on financing terms, and on taxes. This paper derives this price-rent multiple in terms of these variables and calculates its value from 1963 through 1978. The results indicate that housing prices grew more rapidly than rents and the CPI largely as the result of a 33% increase in the price-rent multiple over those years. This increase in the capitalization rate occurred, despite higher nominal financing terms, because the relative terms of housing finance tended to ease and because the expected growth rate of rents increased more than its discount rate.     

Risk and Return in the U.S. Housing Market: A Cross-Sectional Asset-Pricing Approach

This article carries out an asset-pricing analysis of the U.S. metropolitan housing market. We use ZIP code–level housing data to study the cross-sectional role of volatility, price level, stock market risk and idiosyncratic volatility in explaining housing returns. While the related literature tends to focus on the dynamic role of volatility and housing returns within submarkets over time, our risk–return analysis is cross-sectional and covers the national U.S. metropolitan housing market. The study provides a number of important findings on the asset-pricing features of the U.S. housing market. Specifically, we find (i) a positive relation between housing returns and volatility, with returns rising by 2.48% annually for a 10% rise in volatility, (ii) a positive but diminishing price effect on returns and (iii) that stock market risk is priced directionally in the housing market. Our results on the return-volatility-price relation are robust to (i) metropolitan statistical area clustering effects and (ii) differences in socioeconomic characteristics among submarkets related to income, employment rate, managerial employment, owner-occupied housing, gross rent and population density.     

Modeling Spatial Variation in Housing Prices: A Variable Interaction Approach

The absolute location of each real estate parcel in an urban housing market has a unique location-value signature. Accessibility indices, distant gradients and locational dummies cannot fully account for the influence of absolute location on the market price of housing because there are an indeterminable number of externalities (local and nonlocal) influencing a given property at a given location. Furthermore, the degree to which externalities affect real estate values is not only unique at each location but highly variable over space. Hence, absolute location must be viewed as interactive with other determinants of housing value. We present an interactive variables approach and test its ability to explain price variations in an urban residential housing market. The statistical evidence suggests that the value of location, as embodied in the selling price of housing units, may not be separable from other determinants of value. It is recommended that housing valuation models, therefore, be specified to allow site, structural and other independent attributes to interact with absolute location—{ x , y } coordinates—when accounting for intraurban variation in the market price of residential housing. This approach is especially useful when estimating the value of housing for geographic areas where very little is known a priori about the neighborhoods or submarkets.     

The Spatial Proximity of Metropolitan Area Housing Submarkets

An important question related to housing submarket construction is whether geographic areas must be spatially adjacent in order to be considered the same submarket. Housing consumers do not necessarily limit their search to spatially concentrated areas and may search similarly priced neighborhoods located throughout a metropolitan area when making housing consumption decisions. This article examines two alternative procedures for delineating submarkets: one that combines adjacent census block groups into areas with enough transactions to estimate the parameters of a hedonic house price equation and a second that permits spatial discontinuities in submarkets. The criterion used to evaluate the alternative techniques is the accuracy of hedonic house price predictions.     

Internet Search Behavior,Liquidity and Prices in the Housing Market

We employ detailed internet search data to examine price and liquidity dynamics of the Dutch housing market. We show that the number of clicks on properties listed online proxies demand and the number of listed properties proxies supply. From this internet search behavior, we create a market tightness indicator and we find that this indicator Granger causes changes in both house prices and housing market liquidity. The results of a panel VAR suggest that a demand shock results in a temporary increase in liquidity and a permanent increase in prices in urban areas. This is in accordance with search and matching models.     

The Long‐Run Relationship Between House Prices and Rents

I use standard error‐correction models and long‐horizon regression models to examine how well the rent–price ratio predicts future changes in real rents and prices. I find evidence that the rent–price ratio helps predict changes in real prices over 4‐year periods, but that the rent–price ratio has little predictive power for changes in real rents over the same period. I show that a long‐horizon regression approach can yield biased estimates of the degree of error correction if prices have a unit root but do not follow a random walk, and I construct bootstrap distributions to conduct appropriate inference in the presence of this bias. The results lend empirical support to the view that the rent–price ratio is an indicator of valuation in the housing market.     

A Methodology for Constructing Vacant Land Price Indices

Existing estimates of movements in vacant land prices are limited to a few metropolitan areas and infrequent time intervals. This paper develops a new methodology for estimating vacant land price trends for subareas within states and metropolitan areas. It utilizes data from a sales ratio study, a large database available in most states. The methodology uses assessed value to control for "hedonic characteristics" associated with the property and its location. A model is developed to correct assessed value for measurement errors. Statistical results for forty-one Connecticut towns indicate that the model provides a reasonable compromise between data availability and accuracy of price trend estimates.     

House Prices and Inflation

The present paper examines the long-run impact of inflation on homeowner equity by investigating the relationship between house prices and the prices of nonhousing goods and services, rather than return series and inflation rates as in previous empirical studies on the inflation hedging ability of real estate. There are two reasons for this methodological departure from prior practice: (1) while the total return on housing cannot be accurately measured, the total return on housing is fully reflected in housing prices, and (2) given that using returns or differencing a time series leads to a loss of long-run information contained in the series, valuable long-run information can be captured by using prices. Also, unlike previous related studies, we exclude housing costs from goods and services prices to avoid potential bias in estimating how inflation affects housing prices. Monthly data series are collected for existing and for new house prices as well as the consumer price index excluding housing costs for the period 1968–2000. Based on both autoregressive distributed lag (ARDL) models and recursive regressions, the empirical results yield estimated Fisher coefficients that are consistently greater than one over the sample period. Thus, we infer that house prices are a stable inflation hedge in the long run.     

Explaining the Variability of Apartment Rents

The research reported here uses regression analysis to analyze rent variations in a sample of apartment data from the Phoenix metropolitan area. Many of the variables used in hedonic price studies of houses are found to be significant in explaining variations in apartment rent. There are differences between hedonic studies of houses and apartments particularly with respect to common area features or amenities. The analysis of various submarkets also produced interesting results. Various uses can be made of the results of this and similar studies by appraisers (market-derived adjustments), property managers (setting rents) and feasibility analysts (the design of apartment projects).     

Commuting with Lost Housing Services as the Opportunity Cost

In American metropolitan areas, households are highly mixed by income with higher average incomes at greater distances from downtown. Also, suburbs attract families with children, while poor households and small households with young heads select sites close to the commercial core. These empirical observations and others are predicted by this standard model of a monocentric city with three major modifications. Time at work is controlled by employers, not employees. Households with more members at home consume in the same house more housing services. Finally, lot prices need not be proportional to area. In the resulting equilibrium, households are mixed by income and separated by family size. This contrasts with classic urban models where households are separated only by their workers' wage rates.     

Housing Price Indexes from the 1974–1983 SIMSA Annual Housing Surveys

This paper reports residential real estate price indexes computed from the Standard Metropolitan Statistical Area (SMSA) Annual Housing Survey (AHS) for the 1974 through 1983 period. During this ten-year period, the U.S. Bureau of the Census conducted detailed surveys of the housing stock in sixty metropolitan areas in a three to four year cycle. This information is used to compute tenure specific hedonic housing price indexes for: (1) the entire metropolitan housing market; (2) separately for properties located in the central city and in the suburbs (whenever central city locations are identified); and (3) for three points in the dwelling quality distribution-for substandard housing (using the definition employed by the U.S. Department of Housing and Urban Development), for new housing (housing less than three years old and not substandard), and for existing standard quality housing (everything else). In addition, the hedonic prices reported here are adjusted for the finite sample bias introduced when taking the exponential of a lognormally distributed random variable.     

Do Real Estate Prices and Stock Prices Move Together? An International Analysis

The relationship between stock prices and real estate prices has been the subject of substantial debate in both the academic and practitioner literatures. Existing studies have focused on the time series of stock and real estate returns using data from a single country, such as the U.S. By necessity, these studies examine return and price changes over short intervals, creating a bias when property values are smoothed from year to year. Using data from 17 different countries over 14 years, this paper examines the relation between stock returns and changes in property values and rents. Consistent with other country-specific studies, we find that, with the exception of Japan, the contemporaneous relation between yearly real estate price changes and stock returns is not statistically significant. However, when the data are pooled across countries and when we look at longer measurement intervals, a significant relation between stock returns and both rents and value changes becomes apparent. Real estate prices are also found to be significantly influenced by GDP growth rates and provide a good long-term hedge against inflation but a poor year-to-year hedge.     

Wages,Housing Prices and Commutes

Whereas economic theory suggests that, all else equal, workers should be willing to accept disamenities such as higher housing costs and longer commutes only if they are compensated with higher wages, little is known about the magnitude of these compensating differentials. In this article, I address this gap in the literature by estimating an empirical model of the relationship between wages, housing prices and commutes that addresses the simultaneous determination of these variables. The results from the empirical models suggest that the wage premia associated with high housing costs and long commutes are substantial. Furthermore, results from baseline models reveal that estimates of these compensating differentials are seriously biased if endogeneity is not addressed.     

Rational Expectations, Market Fundamentals and Housing Price Volatility

This paper derives a forward-looking rational expectations house price model and empirically tests its ability to explain short-run fluctuations in real house prices. A novel approach to proxying the imputed rents of owner-occupied housing, as a function of observable housing market fundamentals, is combined with a housing market arbitrage relation to derive a present value model for real house prices. Tests of the rational expectations, nonlinear cross-equation restrictions reject the joint null hypothesis of rational expectations and the asset-based housing price model for quarterly, single-detached house prices in the city of Vancouver, British Columbia from 1979–1991. The model fails to fully capture observed house price dynamics in two real estate booms but tracks real house prices well in less volatile times, suggesting that prices may temporarily deviate from fundamental values in real estate price cycles.     

User Cost and the Demand for Housing Attributes

A number of studies have related changes in the demand for housing to changes in user cost. All have treated housing as a composite good rather than as a bundle of characteristics. We consider the effect of changing user cost on the demand for the component characteristics of owner-occupied housing, and, given information about the supply of the characteristics, we predict implicit price responses. An empirical test of our model indicates that reductions in user cost result in higher real prices for the non-replicable attributes of housing, examples being location and access to fixed amenities. In contrast, the price of attributes that are perfectly elastic in supply are not affected by changes in user costs. We conclude that the effects of changing user cost are not uniform across housing types and locations, thus generating the appearance of housing submarkets.     

The Value of Access to Rail Transit in a Congested City: Evidence from Housing Prices in Beijing

Using unique data sets of Beijing's congestion patterns and housing prices, I find that consumers are willing to pay significantly more for access to rail transit in more congested areas. Transit accessibility, however, offers little travel advantage outside of dense urban areas. The expansion of the metro network mitigates the costs of road congestion, creating both private and social benefits. Two policy initiatives aimed at reducing congestion are found to have achieved positive value effects. Further analysis reveals heterogeneous demand for accessibility, with wealthier residents and those owning fewer cars paying a higher premium for access to rail transit.     

Do the urban poor face higher food prices? Evidence from Vietnam

Whether there is a poverty penalty, in terms of food prices, is unsettled in the literature after more than four decades of study. Unit values from household surveys suggest that prices vary with income while outlet surveys typically find food prices varying with store type but not with neighborhood income. Most outlet surveys are from rich countries, with just one spatially limited study from a developing country. In this paper we use especially collected food price data from metropolitan areas of Vietnam to test whether the urban poor face higher food prices. Food prices in low-income neighborhoods are 1% lower, on average, than in other neighborhoods. Unit values give a different answer to the question of whether the poor face higher prices and are not suited to answer such a question.