Housing Supply,Land Costs and Price Adjustment

We analyze relationships between housing supply elasticities, land costs and house price dynamics, contributing three main insights. First, higher housing supply elasticities help contain short‐run price spikes following demand shocks. Second, land price dynamics influence this relationship; supply responses are lessened and house price spikes are exacerbated as land prices increase. Third, we estimate a system of regional equations modeling housing supply using a Tobin's‐q specification (incorporating construction and land costs) and show that regional price dynamics are a function of the region's supply elasticity.     

The Relationship between Median and Constant Quality House Prices: Implications for Setting FHA Loan Limits

This paper examines the relationship between the asset price of housing and median sales price. We demonstrate: (1) median house prices (as reported by the National Association of Realtors) overstate the increase in constant-quality house prices by about 2% per year over the 1976–1985 period; and (2) regional differences in median house prices and their rates of increase, respectively, are systematically related to regional differences in real incomes and their rates of increase. We use these results to evaluate the recent proposal to raise the FHA maximum loan limit ceiling from the current ceiling of $124,750 to 95% of the area median house price.     

The Measurement and Determinants of Single-Family House Prices

We assess the conceptual and empirical features of a number of house price series for the United States. We then calculate a measure of the net up-grading of the existing stock of houses that took place during the 1950–1989 period and adjust price indexes for this net increase in quality. Judgments about the trend, volatility, and determinants of house prices are shown to depend crucially on which price series is used. The Freddie Mac upgrade-adjusted house price measure rose 5.7% over the past four decades, falling 7.7% from 1950 through 1970 before rising 14.5% from 1970 through 1989. Real house prices declined in the early 1980s due to the increase in real after-tax interest rates and the decline in real materials costs. The recovery of house prices in the late 1980s is attributed to lower unemployment and real after-tax interest rates and particularly to demographic factors associated with the aging of baby boomers.     

基于ESDA方法与空间计量模型的房价溢出效应分析

一个地区房价的波动可以通过时间滞后传递到其他地区，从而产生房价溢出效应。本文以我国31个省际区域2005～2014年的数据为样本，运用探索性空间数据分析（ESDA）对31个省的房价空间分布格局及演变态势进行分析。然后运用空间计量模型分析我国省际房价变动的影响因素，并从地理因素和经济因素两个方面研究房价溢出效应。研究结果表明：中国31个省域间的房价存在明显的空间相关性；地区的城市化率、城镇就业人员平均货币工资、二、三产业产值占GDP比重对房价有显著的影响，房价存在显著的空间溢出效应，经济距离对地区房价影响比地理距离的影响更显著。     

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.     

Nature of comovements in US state and MSA housing prices

This study analyzes the time-varying nature and determinants of comovements in US housing prices using state and metropolitan statistical area (MSA) data. We employ dynamic factor models with time-varying loadings and stochastic volatility (DFM-TV-SV) to estimate the national, regional, and state factors. The time-varying factor loadings and stochastic volatility features enrich the dynamic factor model structures and are an effective tool to examine the comovements in housing prices. We find that the national factor is the dominant factor in explaining the movement of housing prices. The national factor accounts for 79% of the variation in state-level housing prices on average, with the greatest magnitude occurring during the housing boom and bust periods in many regions and states. We also find that the factors and synchronization effects are time-varying and heterogeneous across regions. The state-level housing prices contain higher national housing factor components in states with more diverse economies, higher wages and house prices, and lower unemployment rates. These findings shed light on the effectiveness of residential real estate diversification across the United States and the potential for elevated national housing risk amid economic downturns due to increased national housing price integration.     

Housing Attributes Associated with Capital Gain

Housing units are heterogeneous goods. Rates of change in housing prices are typically modelled as if they arise from factors unrelated to the housing unit itself. For example, housing price increases in the latter part of the 1970s and early 1980s are argued to have arisen primarily from demographic factors and the differential effects of inflation on the effective rate of taxation on income from corporate capital and on owner-occupied housing. Cross-sectional variation in price inflation is not addressed. Consumers who purchased housing units are not indifferent to their attributes. To the extent that expectations vary within regional housing markets as a consequence of variation in housing attributes, standard linear hedonic price regression may generate biased estimates of implicit prices. This paper identifies sufficient conditions for the estimates of implicit prices in linear hedonic price regressions to be unbiased and generate unbiased estimates of implied price changes. Finally, this paper identifies living space (house size) as a significant attribute related positively to the increase in individual housing prices in a regional market.     

我国商品房市场量价关系的实证分析

目前对房地产市场的研究主要集中于房价,而对量价关系的研究较少。鉴于此,文章利用1998～2008年的全国商品房季度数据,对我国商品房市场量价关系进行实证研究,并得出以下结论：从长期来看,我国商品房市场的交易量与价格之间存在协整关系;因果检验显示交易量是价格的格兰杰原因,反之则不成立;通过脉冲响应函数发现外界冲击导致了量价的一致波动且交易量对外界冲击的响应比房价更敏感。     

The Accuracy of Owner-Provided House Values: The 1978–1991 American Housing Survey

The American Housing Survey (AHS) includes the owner's valuation of the house as a measure of the house's value. If owner-stated values are accurate, the AHS (as well as other survey instruments) can be used by researchers studying a variety of topics. In this study we use the metropolitan version of the AHS for three cities over fourteen years to compare owners' valuations with sales prices of houses that sold in the twelve months prior to an interview. We find that, on average, recent buyers report house values that are 8.4% higher than the stated sales prices. Further analysis indicates that these recent buyers, when compared with owners with longer tenure, overvalue their houses by 3.3%, on average. Thus, we find that the average owner overvalues his house by 5.1%. Also, differences between sales prices and owners' valuations are not related to particular characteristics of the house, occupants (other than length of tenure), or neighborhood. Thus, the use of the owners' valuations will result in accurate estimates of house price indexes and will provide reliable estimates of the prices of house and neighborhood characteristics.     

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.     

On the Nature of Self‐Assessed House Prices

In models of optimal household behavior, the value of housing affects consumption, savings and other variables. But homeowners do not know the value of their house for certain until they sell, so while they live in their home they must rely on local house price data to estimate its value. This article uses data from the recent housing boom and bust to demonstrate that changes in households' self‐assessed home values are strongly consistent with the predictions of a model in which households optimally filter available house price data. Specifically, we show that self‐assessed house prices did not increase as rapidly as house price indexes during the boom and did not decline as severely during the bust. A Kalman filter model nearly perfectly replicates these data. These findings have direct implications for economists studying asking prices during booms and busts, optimal default decisions and other key housing‐related phenomena.     

Local impacts of a global crisis: Food price transmission,consumer welfare and poverty in Ghana

This paper takes a local perspective on global food price shocks by analyzing food price transmission between regional markets in Ghana. It also assesses the impacts of food price increases on various household groups. Taking the 2007–2008 global food crisis as an example, we show that prices for domestic grain products are highly correlated with world market prices. This is true both for products for which Ghana is highly import-dependent (e.g., rice) and the products for which Ghana is self-sufficient (e.g., maize). The econometric results also show that price transmission is high between regional producer markets and markets located in the country’s largest cities, and the distance between producer and consumer markets and the size of consumer markets matter in explaining the price transmission. The welfare analysis for households as consumers shows that the effect of world food prices appears relatively modest for the country as a whole due to relatively diverse consumption patterns within country. However, the national average hides important regional differences, both between regions and within different income groups. We find that the poorest of the poor—particularly those living in the urban areas—are hardest hit by high food prices. The negative effect of the food crisis is particularly strong in northern Ghana. The main explanations for this regional variation in the price effect is the different consumption patterns and much lower per capita income levels in the North of Ghana compared to other regions in the country.     

Do House Price Levels Anticipate Subsequent Price Changes within Metropolitan Areas?

This research examines the relationship between hedonically controlled housing price levels and subsequent changes in those prices across locations within MSAs. Are areas with a high price relative to an “imputed rent” paying for higher appreciation? In an efficient market (e.g., Gordon Growth Model), as fundamentals (impute rent) differ across locations and change over time, anticipation of these should generate a positive correlation between (residual) price levels and subsequent price changes. We undertake these tests in four different MSAs using a panel of repeat‐sale house price indices that have been scaled to price levels with the hedonic attributes of the house and ZIP code. In three markets we find that identical houses in higher priced ZIP codes subsequently appreciate faster. In one market we find that there is little statistical difference.     

Assessing the Market Value of Real Estate Property with a Geographically Weighted Stochastic Frontier Model

In this study we consider the problem of sellers, buyers and real estate appraisers in determining the price for a house, taking into account the characteristics of the house and its location as well as the goals of these three different parties. The appraiser's job is to determine the fair market value of the house, while the buyer and seller want to find, respectively, the lowest and highest feasible price for it. We combine recent developments in geography and econometrics to develop an approach that determines local estimates of property values from the perspectives of the buyer, seller and appraiser, taking into account the characteristics of the house as well as its location. We illustrate our approach analyzing closing prices in one residential real estate market.     

Discount Brokerage in Residential Real Estate Markets

Transaction costs are thought to affect asset prices and market liquidity, but the direction and magnitude of these effects continue to be the subject of debate. In the single‐family residential market, discount brokers offer to list a house for a lower price and thus reduce the transaction costs associated with obtaining a match. In this article we obtain empirical estimates of the price and liquidity impact of a seller selecting a discount broker to market a single‐family residential property. The unique data set allows for the identification of residential properties that were listed by a discount brokerage firm. The empirical results confirm the predictions of our theoretical model. Using a sample of 318,221 listings and 243,625 sales, we find that houses listed by discount brokers sell at prices similar to non‐discount brokerage listings, but are less likely to sell, and when they do sell, take approximately three days longer to sell. The results indicate that lower transaction costs do not impact housing prices in this market, but that they are related to asset liquidity.     

Canadian Inter-City House Price Differentials

The purpose of this paper is to identify the sources of intercity house price differentials in Canada. The results indicate that demand factors are important explanatory variables; a 1% increase in the income of households raises house prices by 1.11%; higher rates of anticipated inflation result in higher house prices as households increase their demand for real assets such as housing during inflationary periods; and finally, the fraction of households that are non-family households is positively associated with house prices. These results are in agreement with those of other countries.     

Border Effects in House Prices

This article estimates the effect of the Dutch–German border on house prices. We argue that the difference between house prices at the border indicates the willingness to pay to stay in a country compared to living across the border. After a change in the tax rules in 2001, migration from the Netherlands to Germany increased substantially and the gradient of Dutch house price towards the German border steepened. Combining a German and Dutch real estate dataset and using different estimation strategies, we find that asking prices of comparable housing drop by about 16% when one crosses the Dutch–German border.     

Private Labels,National Brands and Food Prices

We study the relationships between national brand prices and the development of private labels, using home-scanned data from a consumer survey reporting purchases for 218 food products. When the impact of private label development is significant (116 cases out of 218), we observe a positive correlation (89%) between brand price and purchases of private labels. When controlling for changes in product quality, we still find a positive relation between private label development and national brand prices. Thus, the change in the national brand product characteristics only partly explains the increase in the national brand prices. Furthermore, the price reactions of national brands differ according to the type of private labels they face. Finally, we demonstrate that the development of private labels has less effect on the prices of second-tier brands than on the prices of the leading brand.      

Oil Prices and Urban Housing Demand

We develop a model of a monocentric, oil‐exporting city. The model predicts a “twist” (rotation combined with a level shift) of the house price gradient with an oil price change due to the combined producer price and transportation cost effects. Empirical findings support the predictions, with house price changes positively linked to the price of oil in cities specialized in oil and gas‐related industries, and negatively linked in suburban areas of all cities. These results quantify the large and differential risks to house prices associated with oil price changes both within and across cities. Overall, estimates suggest a 50% change in the price of oil results in a city‐wide house price change of 15% over five years in a city specialized in the production of oil (export employment share of 50%), whereas house prices for units greater than 15 miles from the city‐center change in relative terms by ?1.5% over the same period.     

Cash to spend: IPO wealth and house prices

This study demonstrates the impact of initial public offerings (IPOs) on local house prices. Applying spatial difference-in-differences methods to IPOs in California from 1993 to 2017, we find house prices increase by 0.7%–0.9% near an IPO firm's headquarters around filing and issuing dates. Upon lockup expiration, price changes depend on postissuance returns. Treating the San Francisco Bay as a commuting barrier, we identify sustained price increases after filings and temporary increases after issuing and lockup expiration. We also confirm post-IPO price divergence between the treatment and synthetic control areas. Our findings indicate the effect of liquid wealth under mild financial constraints.