Valuing Open Space and Land-Use Patterns in Urban Watersheds

This article presents the results of a hedonic property value analysis for an urban watershed in New Haven County, Connecticut. We use spatially referenced housing and land-use data to capture the effect of environmental variables around the house location. We calculate and incorporate data on open space, land-use diversity, and other environmental variables to capture spatial variation in environmental quality around each house location. We are ultimately interested in determining whether variables that are reflective of spatial diversity do a better job of describing human preferences for housing choice than broad categories of rural versus urban areas. Using a rich data set of over 4,000 houses, we study these effects within a watershed that includes areas of high environmental quality and low environmental quality as well as varying patterns of socioeconomic conditions. Our results suggest that, in addition to structural characteristics, variables describing neighborhood socioeconomic characteristics and variables describing land use and environmental quality are influential in determining human values. We also find that the scale at which we measure these spatially defined environmental variables is important.     

The Transitory and Legacy Effects of the Rental Externality on House Price and Liquidity

It is widely believed that tenant-occupied houses do not show as well as owner-occupied or even vacant units and so are harder to sell. These short term or transitory marketing effects should disappear in subsequent sales by owner-occupiers. Overuse by tenants and poor maintenance by landlords, however may lead to longer term or legacy effects on value and liquidity. We use a 20 year data series on house transactions to estimate these separate effects in a simultaneous model of price and liquidity. The results reveal strong transitory renter effects on both value and liquidity consistent with lower buyer willingness-to-pay. We do not find persistent legacy effects from prior use as rental property. Instead, there appears to be unmeasured quality or a characteristic common to houses suitable for rent that leads to permanently lower market values regardless of previous use in that capacity.     

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.     

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.     

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.     

Predicting House Prices Using Multiple Listings Data

It is often necessary to accurately predict the price of a house between sales. One method of predicting house values is to use data on the characteristics of the area's housing stock to estimate a hedonic regression, using ordinary least squares (OLS) as the statistical technique. The coefficients of this regression are then used to produce the predicted house prices. However, this procedure ignores a potentially large source of information regarding house prices—the correlations existing between the prices of neighboring houses. The purpose of this article is to show how these correlations can be incorporated when estimating regression coefficients and when predicting house prices. The practical difficulties inherent in using a technique called kriging to predict house prices are discussed. The article concludes with an example of the procedure using multiple listings data from Baltimore.     

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.     

The Effects of Attributes on the Repeat Sales Pattern of Residential Property in Hong Kong

There has been copious research work on the development of house price models and the construction of house price indices. However, results in some studies revealed that the accuracy of such indices could be subject to selection bias when using only information from a sample of sold properties to estimate value movements for the entire housing stock. In particular, estimated house price appreciation is usually systematically higher among properties that change hands more frequently. It therefore suggests that the determination of important factors affecting the transaction frequency or intensity of a housing unit should be a more fundamental research question. This paper examines the possible factors that determine the popularity of residential unit by means of a repeated sales pattern. The Poisson regression model and event history analysis techniques are employed to assess the effect of attributes on transaction frequency and intensity. The event history analyses technique can take account of transaction-specific as well as time-dependent covariates, and therefore is recommended for analyzing repeated sales data in a real estate market. All transaction records during the period 1993–2000 from the Land Registry of one of the most popular residential estates in Hong Kong were used to illustrate the method. Unlike a response to favorable transaction price, good quality units do not necessarily inherently display a high transaction frequency. Rather, units of average quality are more likely to be transactionally active.     

The Trade-off Between the Selling Price of Residential Properties and Time-on-the-Market: The Impact of Price Setting

When a house is placed on the market, the seller must choose the initial offer price. Setting the price too high or too low affects the marketability of the property. While there is near universal agreement that the seller faces a trade-off between selling at a higher price and selling in less time, there is less agreement about how to measure this trade-off. This paper offers a framework for analysis and shows that an increase in the list price increases expected time-on-the-market (TOM). Because house buyers must solve a type of signal extraction problem, the effect of a higher list price is magnified for houses in a market segment having a low predicted variance of the list price. This paper also shows that the list price of houses which are withdrawn before sale has a higher mean and variance, and that the possibility of withdrawal censors information about the time-on-the-market.     

The determinants of analyst-firm pairings

This paper explores the determinants of observed analyst-firm pairings. We adopt an analyst/brokerage house perspective that allows us to examine not only firm-level characteristics as in prior research, but also attributes of the analyst and the analyst’s brokerage house that may drive these pairings. Our empirical analyses provide two primary insights. First, analyst characteristics such as industry expertise and relative experience, and brokerage house characteristics such as continuity of coverage, are associated with the decision to follow a firm. Second, there is substantial variation in the association between firm, analyst, and brokerage house characteristics and the decision to follow a firm; this occurs across individual analysts as well as across different types of brokerage houses. Overall, our results provide further insights into the factors leading to observed analyst-firm pairings, and indicate that these factors vary across analysts and their brokerage houses – suggesting richer associations than the average firm-level relationships documented by prior research.     

The Dependency of Rent-to-Price Ratio on Appreciation Expectations: An Empirical Approach

Using two unique datasets from different neighborhoods in Houston, TX, which provide us data for houses with similar structure (or even same house), we test the standard model of housing values to determine how the formation of households’ expectations regarding price appreciations affects housing market prices. Using these datasets we are able to address previously encountered problems in the literature such as the lack of adjustment for quality differences, the connection between prices and rents for the different type of housing, and the spatial distribution of housing. We test whether consumer behavior leads to potentially unstable market conditions with price bubbles. Our results suggest that appreciation expectations are based on past price appreciation but at the same time they depend on the fundamental factors such as, locational and structural. These findings show a hybrid consumer behavior of rational and adaptive expectations. Finally, we show how these expectations could sometimes lead to unstable price levels.     

住房投资与家庭创业:促进还是挤出?--基于CFPS面板数据的实证分析

本文基于房价持续增长的现实背景,利用中国家庭追踪调查数据,以家庭住房数量为核心检验住房投资对城镇家庭创业选择的影响及作用机制。研究发现,相比无房家庭,自有住房家庭的创业概率并未显著提高;当家庭有多套住房时,才能显著提高创业概率。同时,对仅有一套住房的家庭,住房价值对家庭创业没有显著影响。但对有多套住房家庭而言,住房价值能显著提高创业概率。本文发现住房投资尽管能够通过缓解信贷约束、增加风险偏好等机制促进创业,但也会对创业产生显著的挤出效应。只有在政府坚持住房去金融化和"房住不炒"的调控政策下,住房投资对家庭创业的促进作用才能逐步占据主导。     

Spatial Competition and Shopping Externalities: Evidence from the Housing Market

In search markets, greater spatial concentration of sellers increases price competition. At the same time, though, a greater concentration of sellers can create a shopping externality by attracting more buyers to the site. Using housing sales data, we test for spatial competition and shopping externality effects on prices and marketing time. We find that they reflect both competitive and shopping externality effects from surrounding houses, although the relative strength varies with how fresh the house is in the market, the freshness of surrounding houses, and the phase of the market cycle. New listings have the strongest shopping externality effect on neighboring houses that have been on the market for some time. Vacant houses have their strongest competition effects in the declining market and externality effects in the rising market. Fresh houses on the market reap little benefit from shopping externalities in all phases of the market cycle.     

Do private firm valuations contain incremental information content over routine analyst valuations?

In this study, we seek to investigate whether private expert valuations commissioned for specific transactions outside the exchange contain incremental information content over public analyst valuations published routinely by investment houses. First, we find that public valuations are based to a larger extent on financial statements and market quotes, whereas private valuations tend to be based on other, non-public information. Second, we show that investors’ response to both public and private valuations is cautious in the short-run as well as in the long-run. Third, we provide evidence that despite the fact that private valuations have the advantage of time, human resources, and better access to non-public information, they do not provide different results than those obtained from public valuations. We conclude that while private valuations may be captured as more accurate and reliable, their superiority over public valuations is questionable at best.     

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.     

Is there a Difference? The Performance Characteristics of SRI Equity Indices

Abstract:  This study analyses whether stock indices that represent socially responsible investments (SRI) exhibit a different performance compared to conventional benchmark indices. In contrast to other studies, the analysis concentrates on SRI indices and not on investment funds. This has several advantages, since transaction costs of funds, the timing activities and the skill of the fund management do not have to be considered. A direct measure of the performance effects of SRI screens is therefore examined. The 29 SRI stock indices are analysed by single-equation models as well as by multi-equation systems that exploit the information in the cross-section. SRI stock indices do not exhibit a different level of risk-adjusted return than conventional benchmarks. But many SRI indices have a higher risk relative to the benchmarks. The findings are robust to the use of different benchmark indices and apply to all common types of SRI screening.     

When to Lean against the Wind

In this paper, we show that policymakers can distinguish between good and bad credit booms with high accuracy and they can do so in real time. Evidence from 17 countries over nearly 150 years of modern financial history shows that credit booms that are accompanied by house price booms and a rising loan‐to‐deposit ratio are much more likely to end in a systemic banking crisis than other credit booms. We evaluate the predictive accuracy for different classification models and show that characteristics observed in real time contain valuable information for sorting the data into good and bad booms.     

Stochastic correlation across international stock markets

This paper examines the correlation across a number of international stock market indices. As correlation is not observable, we assume it to be a latent variable whose dynamics must be estimated using data on observables. To do so, we use filtering methods to extract stochastic correlation from returns data. We find evidence that the estimated correlation structure is dynamically changing over time. We also investigate the link between stochastic correlation and volatility. In general, stochastic correlation tends to increase in response to higher volatility but the effect is by no means consistent. These results have important implications for portfolio theory as well as risk management.     

Neighborhood Street Layout and Property Value: The Interaction of Accessibility and Land Use Mix

This paper evaluates how consumers value differences in neighborhood composition and street layout, factors not previously included in empirical studies of house value. Highly connected street patterns are important to New Urbanism. We use measures of neighborhood street connectivity and their interaction with other neighborhood attributes to evaluate how street layout affects property values. We employ two different methods of indexing street layout. Both methods show layout has a significant impact on price, but conclusions are sensitive to the method used. In pedestrian oriented neighborhoods, a more gridiron-like street pattern increases house value using one measure, but greater connectivity decreases house value using the other. In auto-oriented developments, a more gridiron-like street pattern reduces house value using either measure.

Selectivity,Quality Adjustment and Mean Reversion in the Measurement of House Values*

This paper develops a model of price formation in the housing market which accounts for the non-random selection of those dwellings sold on the market from the stock of existing houses. The model we develop also accounts for changes in the quality of dwellings themselves and tests for mean reversion in individual house prices. The model is applied to a unique body of data representing all dwellings sold in Sweden's largest metropolitan area during the period 1982–1999. The analysis compares house price indices that account for selectivity, quality change and mean reversion with the conventional repeat sales models used to describe the course of metropolitan housing prices. We find that the repeat sales method yields systematically large biased estimates of the value of the housing stock. Our comparison suggests that the more general approach to the estimation of housing prices or housing wealth yields substantially improved estimates of the course of housing prices and housing wealth.