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.     

Short Holds,the Distributions of First and Second Sales,and Bias in the Repeat-Sales Price Index

The Journal of Real Estate Finance and Economics -     

Sample Selection Bias and Repeat-Sales Index Estimates

Analysis of variations in house values among localities requires reliable house value indices. Gatzlaff and Haurin (1994) indicate that traditional hedonic house value index estimates, using only information from a sample of sold homes to estimate value movements for the entire housing stock, may be subject to substantial bias. This article extends previous work by adapting the censored sample procedure to the repeat-sales index estimation model. Using data from Dade County, Florida, a house value index constructed from a sample of homes selling more than once, rather than all houses in a locality, is found to be biased. The bias is shown to be highly correlated with changes in economic conditions.     

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.     

Aggregation of Local Housing Markets

This article explores the implications of spatial aggregation for parameter estimates in models of the housing sector. Using illustrative static and dynamic models and realistic assumptions about the true parameter values at the level of the local market, the article characterizes and quantifies the bias resulting from spatial aggregation and then uses the results to shed light on some previous findings from aggregate modeling of the housing sector.     

Bias and Precision of Estimates of Housing Investment Risk Based on Repeat-Sales Indices:A Simulation Analysis

A simulation analysis is reported which examines the bias and precision of estimates of housing investment risk based on small sample indices of housing returns. The trade-off between smoothing bias (due to temporal aggregation in the index) and noise bias (induced by random estimation error) is examined in the housing return total volatility, beta, and autocorrelation statistics of the index returns. The study compares the performance of three different specifications of the repeat-sales index, under assumptions of either an informationally efficient or inefficient housing market, and at two levels of estimation data availability. Findings suggest that regression-based repeated-measures indices may be useful at a more micro-level (e.g., at the neighborhood level or for specific housing types) than has hitherto been employed.     

Controlling for Quality in House Price Indices

Given the importance of house prices it is not surprising that house price indices are used for many purposes. One of the factors that differentiates these indices is the house price determinants (such as structural characteristics and neighborhood quality) that are accounted for—that is, held constant. Indices are usually generated from house price regressions. It is shown that, regardless of the desired level of accounting, it is necessary to control for all significant determinants of house prices in these regressions to obtain unbiased estimates of the growth in house prices. An empirical example shows that not controlling for neighborhood quality can lead to substantial biases in estimates of house price appreciation rates even if the index does not account for this factor.     

Alternative Methods of Increasing the Precision of Weighted Repeat Sales House Prices Indices

Weighted repeat sales house price indices have become one of the primary indicators used to identify housing market conditions and to estimate the amount of equity homeowners have gained through house price appreciation. The primary reason for the acceptance of this methodology is that it derives a location specific (typically, census division, state or metropolitan area) average change in house prices from repeated observations of individual house prices. It is this repeat attribute that allows repeat sales price indices to claim that it is a preferable index which does a better job of holding quality constant. The amount of time between the two observed prices for a single property is determined by when the home transacts. Some homes transact twice in a period of months and others do not transact for decades. It is likely that individual house price appreciation rates vary from the mean appreciation rate, as estimated by the index, in a systematic fashion. In general, the longer the time between transactions the more variance there is in individual house price appreciation. This paper extends this concept to include new dimensions. For instance, houses that appreciate faster than the mean, as estimated by the index for that location, may experience a different variation structure than homes that appreciate slower. This process can be viewed as an asymmetric treatment of the variance of house price appreciation around the estimated index. In addition, the variance of expensive and affordable homes may also be different and time varying. This paper finds evidence that adding the dimensions of price tiers and asymmetry to the variance estimate has merit and does affect the estimated index as well as homeowner equity estimates. Homeowner equity estimates are especially sensitive to these added dimensions because they depend on both the revised index and the estimated variances, which are specific to each dimension considered—time between transaction, asymmetry, and price tier.     

An Empirical Study of Sample-Selection Bias in Indices of Commercial Real Estate

Real estate price indices based solely on samples of sold properties may not accurately represent the population of properties due to potential sample-selection bias. This study addresses this potential for sample-selection bias in the construction of commercial price indices within the context of the Phoenix area office market. The empirical analysis confirms the presence of sample-selection bias in the estimation of the total price equation. However, within this sample, the price indices generated after correcting for sample-selection bias do not appear significantly different from those that do not consider selectivity bias.     

Three New Real Estate Price Indices for Geneva,Switzerland

This paper develops constant-quality price indices for three categories of real estate-apartment buildings, vacant land, and condominiums—for the city of Geneva, Switzerland. We use both the hedonic and repeat sales models to estimate the price level and, in turn, the rate of price change. The general pattern of each series suggests that real estate prices in Geneva were fairly stable throughout the 1970s, increased sharply during the 1980s, but gave back some of these gains in the early 1990s. Interestingly, the sharp rise in prices in the second half of the 1980s is very similar to that found in some regions of the United States. We also consider the problem, implicit in the repeat sales method, of revisions in previously estimated price indices as additional data become available in later years.     

Information Aggregation Bias and Samuelson's Dictum

Under the assumption of incomplete information, idiosyncratic shocks may not dissipate in the aggregate. An econometrician who incorrectly imposes complete information and applies the law of large numbers may be susceptible to information aggregation bias. Tests of aggregate economic theory will be misspecified even though tests of the same theory at the microlevel deliver the correct inference. A testable implication of information aggregation bias is “Samuelson's Dictum” or the idea that stock prices can simultaneously display “microefficiency” and “macroinefficiency;” an idea accredited to Paul Samuelson. Using firm-level data from the Center for Research in Security Prices, we present empirical evidence consistent with Samuelson's dictum. Specifically, we conduct two standard tests of the linear present value model of stock prices: a regression of future dividend changes on the dividend-price ratio and a test for excess volatility. We show that the dividend price ratio forecasts the future growth in dividends much more accurately at the firm level as predicted by the present value model, and that excess volatility can be rejected for most firms. When the same firms are aggregated into equal-weighted or cap-weighted portfolios, the estimated coefficients typically deviate from the present value model and “excess” volatility is observed; this is especially true for aggregates (e.g., S&P 500) that are used in most asset pricing studies. To investigate the source of our empirical findings, we propose a theory of aggregation bias based on incomplete information and segmented markets. Traders specializing in individual stocks conflate idiosyncratic and aggregate shocks to dividends. To an econometrician using aggregate data, these assumptions generate a rejection of the present value model even though individual traders are efficiently using their available information.     

Price Indices Based on the Hedonic Repeat-Sales Method: Application to the Housing Market

Shiller (1993) proposes the hedonic repeated-measures (HRM) approach to measuring constant quality price indices for heterogeneous assets such as some bonds and real estate. We derive a mathematical relationship between the coefficients of the HRM model and those from the standard repeat-sales model, and we demonstrate how hedonic characteristics should be chosen for inclusion in the HRM model. Empirical estimates using Fairfax, Virginia, housing transactions data show that the HRM price index evaluated at the mean of the hedonic variable is virtually identical to the standard repeat sales index, just as predicted by our mathematical relationship. But the HRM allows estimation of different price paths for heterogeneous assets. We demonstrate that use of assessed value as the only hedonic characteristic allows parsimonious HRM estimates.     

Temporal Aggregation in Repeated Sales Models

Over the years, repeated sales models have come to wide and even commercial use. However, considering the subset of dwellings sold twice entail several challenges. Small sample problems constitute a special concern in repeated sales models, since sample sizes tend to be smaller than hedonic methods based on all transactions in a given period of time. Moreover, a cluster of observations in one time period does not only influence the index corresponding to that particular time period, but all other estimated indexes. A simulation approach is used to study the interplay between sample size and temporal aggregation. The analysis shows that serious mis-measurements may occur even in cases where the statistical diagnostic tools like R ² and t-values and empiric standard deviations indicate good explanatory power. However, the risk of serious biases driven by sparse data sets tends to be small, even if the actual estimated price curve show signs of under-smoothing. Mis-measured curves have unstable estimates with respect to temporal aggregation. Two repeated models, one with a slightly finer time partition achieved by adding one more time dummy, used on the same sample can alter the index estimate at a given time with as much at 10–15%. The simulations reveal that varying temporal aggregation is a powerful diagnostic tool and should be employed routinely. The last part of the paper shows that choosing an appropriate temporal aggregation involves more than merely a balance between under-smoothing and over-smoothing. Efficiency questions tend to be better addressed by a higher temporal aggregation, than a good overall estimation of the price curve alone calls for.      

Frequency of Transaction and House Price Modeling

This article examines the characteristics and price behavior of repeatedly transacted properties. Using data from four U.S. counties, we estimate hedonic price models of properties grouped by transaction frequency, and compare estimated standard deviations and estimated appreciation rates by group.For each of four counties studied, we find that estimated house price appreciation is systematically higher among properties that transact more frequently. One possible explanation for this result is that purchasers make property improvements that are not adequately reflected in the available data.We also find that estimated standard deviations of the disturbance term show a marked decrease as the frequency of transaction increases. Since frequently transacting properties are not found to be systematically more homogeneous than seldomly transacting properties, we do not attribute this to any increase in homogeneity for frequently transacting properties, but rather to the length of time elapsed between transactions of properties.The findings of this article suggest that repeat-sales price models may need to be adjusted to account for cross-sectional variation in transaction probabilities---that is, the selectivity of the subsample of properties that transacted (or transacted repeatedly) during any finite study period.     

Developing a House Price Index for The Netherlands: A Practical Application of Weighted Repeat Sales

This paper describes the development of a house price index that has been introduced in May 2005 in The Netherlands. This monthly index, called Woningwaarde Index Kadaster (House Price Index Kadaster), is designed to detect changes in the price of the overall stock of owner-occupied homes. Fifty-five indices are calculated: one overall index, four regional indices, 12 provincial indices and 38 indices based on combinations of region/province and dwelling type. We used Case and Shiller’s geometric Weighted Repeat Sales Model to calculate monthly house price indices. We used recorded data on the sales of over 500,000 owner-occupied homes in The Netherlands, all representing repeat sales between January 1993 and December 2006. The accuracy of the index was determined using the 95% confidence interval. We observed that accuracy might become a problem in smaller sub samples. Revision volatility was explored by comparing the index values computed from all available data until December 2005 with the index values computed from the data available until December 2006. Our analysis showed that revision volatility does not seem to be a major problem to the index. We also explored heteroskedasticity in the Repeat Sales method but did not find conclusive evidence for the proposed heteroskedasticity. Given our target (a geometric mean index value) and the characteristics of the dataset (very large but without property characteristics) the Repeat Sales Method seems to be adequate for calculating a house price index for The Netherlands.

Analysts' Rationality and Forecast Bias: Evidence from Sales Forecasts

When optimistic forecasts can improve access to management, rational analysts have incentives to issue optimistically-biased forecasts (Lim, 2001). This paper proposes that the extent of this optimistic forecast bias will depend on the forecast's importance to management. If management attaches less importance to a forecasted measure, analysts should decrease their forecast bias because the expected benefits of issuing optimistic forecasts are less. We examine analysts' earnings and sales forecasts, and predict that analysts' optimistic bias will be greater for earnings than for sales. Results are consistent with our predictions and contribute to the evidence that analysts' forecast bias is rational and intentional.     

Aggregation Bias in Estimates of Conditional Conservatism: Theory and Evidence

This paper documents a study about the influence of the aggregation effect on the estimates of models based on the original Basu model – specifically the Ball, Kothari and Nikolaev model (Ball et al., 2013b). We provide an analytical study of the effect, showing that it can produce two biases: an omitted‐variable bias and a truncated‐sample bias. Using separate proxies for good and bad news for each company and year, we estimate the empirical sign and magnitude of those biases. Our results show that the estimates of conditional conservatism based on regressions of (unexpected) earnings on (unexpected) returns, as in the paper by Ball et al., are contaminated by substantial aggregation bias. More specifically, the aggregation effect causes these models to underestimate good‐news timeliness and overestimate bad‐news timeliness, thereby overestimating differential timeliness. Moreover, when we use proxies that provide better control for the aggregation effect, the differential timeliness coefficient tends to 0, showing that the influence of conditional conservatism on the returns–earnings relationship is, at best, marginal.     

住房公积金政策对我国房地产市场的影响--基于SVAR模型的实证研究

本文利用1999年二季度至2010年一季度的数据,选取个人住房公积金贷款利率、全国房屋销售价格和全国房屋租赁价格作为分析变量,构建结构向量自回归模型,运用脉冲响应和方差分解方法,就住房公积金贷款利率对我国房价和房租的影响进行实证分析。结果显示,住房公积金政策对我国房价和房租的影响主要侧重于中长期效应,短期效应并不明显,基于此,提出政策建议。     

Two Decades of Commercial Property Returns: A Repeated-Measures Regression-Based Version of the NCREIF Index

This article documents 20 years of performance of commercial real estate in the United States using a portfolio of properties that comprise the widely followed NCREIF Property Index (NPI). We develop an extension of the repeated-measures regression (RMR) to produce an improved version of the NCREIF Index that eliminates the stale appraisal and seasonality problems. We use this RMR version of the index to examine the magnitude and duration of the of the crash in property values in the early 1990s. The RMR Index is also compared with the NAREIT Index, and property-type subindices are developed using a Bayesian estimator. Finally, it is also shown how the RMR can be used to estimate the average magnitude of random valuation error in commercial property valuation.     

Stock Price and Volume Effects Associated with Compositional Changes in European Stock Indices

This paper provides further evidence of price and volume effects associated with index compositional changes by analysing the inclusions (exclusions) from the French CAC40 and SBF120 indices, as well as the FTSE100. I find evidence supporting the price pressure hypothesis associated with index fund rebalancing, but weak or no evidence for the imperfect substitution, liquidity and information hypotheses. The results improve on recent evidence from the S&P500 index. The evidence for the FTSE100 additions shows, in particular, that markets learn about an imminent inclusion and incorporate this information into prices, even before the announcement date.