Explaining Intermetropolitan Variation in Housing Prices, Rents and Land Prices

In attempting to explain why housing prices, rents and urban land prices vary so dramatically between U.S. metropolitan areas, a simple model of a metropolitan housing market is presented identifying three interrelated submarkets. Estimating equations for rent, housing prices and urban land prices are identified and estimated using two-stage least squares. The empirical results provide strong support for the theoretical model concerning how these three submarkets interact. The results also suggest that household income and construction costs are the most important factors causing housing prices, rents and land prices to vary between metropolitan areas.     

The Interaction of Building Codes and Housing Prices

The study analyzes the effect of restrictive building codes on the price of housing, and the simultaneous impact of housing values on the strictness of codes. A model is defined and estimated, using data for more than 1100 localities. The results show that strict codes raised housing values, in 1970, by about one thousand dollars. They furthermore show that the strictness of codes is in turn affected by housing values, as well as by the strength of construction unions. Homeowners and construction unions are thus both observed to gain from restrictive building codes, which can explain the prevalence of such regulations.     

The Effect of Mortgage Rate Buydowns on Housing Prices:

The model developed in this paper analyzes the effect of builder-financed FHA-VA mortgage subsidies or buydowns on the price of housing. Hedonic pricing equations are estimated for a locationally and qualitatively uniform sample of new tract development homes. The explanatory variables are vectors of physical and financial characteristics. The latter include a continuous variable for discount points paid by builders which is indicative of the magnitude of prepaid finance charges. The results indicate that a substantial portion of mortgage subsidy costs are shifted to buyers in the form of inflated housing prices.     

Forecasting Prices and Excess Returns in the Housing Market

The paper uses quarterly indexes of existing single-family home prices estimated with microdata on properties that sold more than once to estimate excess returns to investment in owner-occupied housing. Housing prices and excess returns are estimated over the period 1970:1 to 1986:3 for Atlanta, Chicago, Dallas, San Francisco. Using time-series cross-section regressions we test for the forecastability of prices and excess returns using a number of independent variables. Price changes in one year tend to continue for more than one year in the same direction. The ratio of construction costs to price, changes in adult population and increases in real per capita income all are positively related to excess returns or price changes over the subsequent year. The results add weight to the argument that the market for single-family homes is inefficient.     

A Regime‐Switching Approach to Modeling Rental Prices of U.K. Real Estate Sectors

This article uses regime‐switching models of the threshold type to analyze the adjustment process of rental prices for three U.K. commercial real estate sectors over the period 1974–2008. The nonlinear models outperform their linear counterparts in in‐sample fit. Their out‐of‐sample forecasting ability is better whenever the corresponding linear models contain a significant amount of neglected nonlinearity. Regime switches are triggered when the growth rates of rental price exceed certain threshold levels. For the industrial and retail sectors such regime switches occur in situations of strong excess demand, for the office sector they occur when there is strong excess supply.     

List Prices,Sale Prices and Marketing Time: An Application to U.S. Housing Markets

Many goods are marketed after first stating a list price, with the expectation that the eventual sales price will differ. In this article, we first present a simple model of search behavior that includes the seller setting a list price. Holding constant the mean of the buyers’ distribution of potential offers for a good, we assume that the greater the list price, the slower the arrival rate of offers but the greater is the maximal offer. This trade‐off determines the optimal list price, which is set simultaneously with the seller's reservation price. Comparative statics are derived through a set of numerical sensitivity tests, where we show that the greater the variance of the distribution of buyers’ potential offers, the greater is the ratio of the list price to expected sales price. Thus, sellers of atypical goods will tend to set a relatively high list price compared with standard goods. We test this hypothesis using data from the Columbus, Ohio, housing market and find substantial support. We also find empirical support for another hypothesis of the model: atypical dwellings take longer to sell.     

Transaction-Based Office Price Indexes: A Spatiotemporal Modeling Approach

This study examines the potential of a two-order spatiotemporal autoregressive model with a Bayesian heteroskedasticity robust procedure in modeling strata-titled Singapore office unit transaction prices and in constructing transaction-based disaggregate office price indexes. The model reduces the problems caused by the infrequent trading of individual commercial properties. However, for those office properties that are located outside the CBD and also for those less frequently transacted, the power of the model in capturing these particular office buildings' price dynamics is limited. The significant differences of the office prices across the various office buildings and submarkets show that the model can capture the variation in office prices and track the timing of capital gains and losses that investors may accrue on spatially distributed office properties more accurately than hedonic or weighted least squares estimates.     

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.     

Long‐Term Growth in Housing Prices and Stock Returns

A firm's long‐term stock returns are negatively related to past growth in housing prices in the state where the firm is located. The housing price effect is persistent and robust to controlling for the long‐term stock return reversal effect, changes in mortgage interest rates across the states, cyclicality in housing prices and overall local economic conditions. There is no evidence that extant asset pricing models can adequately explain the effect. The study discusses potential explanations for, and the implications of, the cross‐regional housing price effect.     

The Impact of Environmental Contamination on Condo Prices: A Hybrid Repeat-Sale/Hedonic Approach

We extend the literature on the impact of externalities using an approach based on a hybrid of hedonic and repeat-sales methods. The externality in question is groundwater contamination in Scottsdale, Arizona. The use of condominium sales allows us to assume that major physical characteristics remain unchanged, but location parameters may be altered by urban growth and development as well as contamination. We find an economically significant discount for properties located in the contaminated area. Interestingly, it does not appear until several years after the contamination becomes publicly known, and it seems to have disappeared before the end of the study period.     

The Impact of Growth Control Regulations on Housing Prices in California*

This paper examines the impact of growth controls on the price of new single-family homes. Four types of growth controls are discussed and each is found to have a significantly different impact on housing prices. Regulations that are imposed by one locality only are first compared to those that are imposed by a locality whose neighbors also control growth. In California, increases in house prices in communities with only local growth controls cannot be distinguished from communities that do not control growth. However, the 1969 to 1976 housing price increase in growth control jurisdictions located in extensively regulated housing markets is significantly higher than in local-only or no-control jurisdictions. In addition, controls that restrict the rate of development are compared to those that specify the quality of development. In the extensively regulated San Francisco Bay area, the 1969 to 1976 housing price increase was 35% higher in rate-controlled communities and 20% higher in quality-controlled communities than in no-control communities.     

The Long-Run Relationship between House Prices and Income: Evidence from Local Housing Markets

Many in the housing literature argue that house prices and income are cointegrated. I show that the data do not support this view. Standard tests using 27 years of national-level data do not find evidence of cointegration. However, standard tests for cointegration have low power, especially in small samples. I use panel-data tests for cointegration that are more powerful than their time-series counterparts to test for cointegration in a panel of 95 metro areas over 23 years. Using a bootstrap approach to allow for cross-correlations in city-level house-price shocks, I show that even these more powerful tests do not reject the hypothesis of no cointegration. Thus the error-correction specification for house prices and income commonly found in the literature may be inappropriate.     

Property Investment and Rental Rate under Housing Price Uncertainty: A Real Options Approach

The conventional wisdom that housing prices are the present value of future rents ignores the fact that unlike dividends on stocks, rent is not discretionary. Housing price uncertainty can affect household property investments, which in turn affect rent. By extending the theory of investment under uncertainty, we model the renter's decision to buy a house and the landlord's decision to sell as the exercising of real options of waiting and examine real options effects on rent. Using data from Hong Kong and mainland China, we find a significant effect of housing price on rent and draw important policy implications.     

Indicators of Local Housing Affordability: Comparative and Spatial Approaches

This paper focuses on the measurement of local housing affordability problems. A number of different housing market indicators are offered that help identify the magnitude and nature of housing affordability problems and their geographic distribution. This interest is prompted by the predominance of housing affordability problems and the severity of the problems for many of the lowest income renter households. In addition, there is significant policy interest in "the national goal that every American family be able to afford a decent home in a suitable environment" (National Affordable Housing Act of 1990). This paper develops measures of the spatial distribution of affordability problems and implements measures of the mismatch between the demand and supply of housing affordable to the lowest income households.     

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.     

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.     

Variation Across Price Segments and Locations: A Comprehensive Quantile Regression Analysis of the Sydney Housing Market

Standard house price indices measure average movements of average houses in average locations belonging to an average price segment and hence obscure spatial and cross‐sectional variation of price appreciation rates even within a single metropolitan area. This article combines penalized quantile regression techniques with the hedonic imputation approach to reveal such kind of variation. The method is applied to house transactions from Sydney between 2001 and 2014. The analysis finds significant variation across sub‐markets over time and in particular during the boom‐and‐bust cycle peaking in 2004. Appreciation rates were highest for suburban, low‐priced and lowest for inner‐city, high‐priced houses.     

Modeling Housing Market Fundamentals: Empirical Evidence of Extreme Market Conditions

This article examines the issues encountered in the modeling of market fundamentals during a period of extreme price behavior. The study analyzes the price behavior of the residential property market in Ireland using a number of alternative methodological approaches in the estimation of fundamental market value. Limitations in conventional models such as an inverted demand model are highlighted, in particular, with regard to diagnostic concerns and the static nature of the model. The use of an error correction framework provides more consistent and robust findings. The analysis does appear to indicate that a substantial premium over fundamental values developed in the Irish market during the late 1990s, reaching a peak in 1999 and 2000. However, in recent years, prices have largely been in line with fundamentals.     

A Closer Look at the U.S. Housing Market: Modeling Relationships among Regions

This article investigates the dynamic interactions among nine U.S. regional housing markets by estimating the multivariate cointegration model using both autoregressive (AR) and moving average (MA) representations over the period from 1975 to 2010. Long‐run results indicate that the extent of convergence among the regional housing markets substantially increased over time and more so after the housing bubble burst in the latter part of 2006. Common stochastic trend analysis reveals that the housing regions of New England, Mid‐Atlantic and the Pacific were the primary regional drivers that led the regions toward long‐run equilibrium during the 1975 to 2006 subperiod. Further analysis indicates that the relationships among the regions cannot be attributed to trends in two important macroeconomic fundamentals: regional per capita income and regional GDP. Finally, short‐run analysis reveals substantial lead lag relationships among all the markets.