The Dynamics of Location in Home Price

Gelfand, Alan E.; Ecker, Mark D.; Knight, John R.; Sirmans, C. F.

doi:10.1023/b:real.0000035308.15346.0a

Cited by 99 publications

(57 citation statements)

References 27 publications

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“…For example, Pace, Barry, Clapp, andRodriguez (1998) andGelfand, Ecker, Knight, andSirmans (2004) 9 Different ways to estimate this model include maximum likelihood (ML) and generalized method of moments (GMM). This analysis uses the SpaceStat (Anselin, 1995) software program to estimate the spatial models.…”

mentioning

confidence: 99%

Finding an Impact of Preservation Policies: Price Effects of Historic Landmarks on Attached Homes in Chicago, 1990-1999

Noonan

2007

Economic Development Quarterly

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The impact of landmark designation on prices of the property and its neighbors sits at the core of the policy debate and empirical research on historic preservation. Yet these studies suffer from serious methodological limitations and biases. First, as important unobserved characteristics likely correlate with landmark designation, an omitted-variable bias results. Second, if designations depend on property values or neighborhood housing market conditions, the endogenous selection process further undermines inferences about preservation policies’ effects. This article outlines more robust empirical strategies and presents new evidence on landmark designation effects on property values. For a sample of Chicago home sales during the 1990s, a hedonic price analysis suggests that landmark buildings and districts sell at a small premium. To address the omitted-variable bias, a repeat-sales approach demonstrates significant spillover effects of landmark designation on prices. These estimates are also robust to sample-selection bias and some forms of spatial autocorrelation.

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confidence: 99%

Finding an Impact of Preservation Policies: Price Effects of Historic Landmarks on Attached Homes in Chicago, 1990-1999

Noonan

2007

Economic Development Quarterly

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“…However, our approach does have some antecedents in the literature. Gelfand et al (1998) and Gelfand et al (2004) propose a model of the evolution of home prices which is similar in spirit to ours. Their model is formulated on the basis of stochastic spatio-temporal processes and estimated using Bayesian methods.…”

Section: Penalized Smoothing Spline Hedonic Modelsmentioning

confidence: 99%

Residential Real Estate, Risk, Return and Home Characteristics: Evidence from Sydney 2002-14

Melser¹,

Rottke²,

Krautz³

et al. 2017

24th Annual European Real Estate Society Conference

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Abstract:Residential real estate is a key component of household wealth. While we have some insight into the aggregate returns of this asset class-thanks to real estate price indexes-we have little understanding of the characteristics of investment in individual properties. This paper outlines and applies a methodology for estimating and examining the variation in risk and return for unique homes. We use large data sets of home prices and rents for Sydney, Australia, from 2002-14, to estimate flexible spline hedonic models which incorporate spatial and characteristics smoothing. Using these models we estimate total returns-the sum of capital gains and rental yield-for a large sample of properties for each time period. This enables use to consider the risk and return of investment in individual parcels of residential real estate and explore the benefits of diversification. As a reference point, we contrast housing with investment in equities. We find that there is dispersion in returns and their volatility-though significantly less than shares-and that this is tied to certain home characteristics. However, when we investigate the benefits of holding a diversified portfolio of homes over a single home we find they are small compared with the corresponding case for equities.

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“…The threshold value is a point at which the semivariogram function achieves a constant value without a successive increase in its value. A general model that would effectively approximate the spatial correlations between property prices in each case is difficult to identify (DUBIN et al 1999;GELFAND et al 2004). A spherical model is generally applied (BASU and THIBODEAU 1998;GILLEN et al 2001), but an exponential model may be more appropriate in some cases due to spatial and planning considerations, and pricing trends on the real estate market (CHICA-OLMO 2007).…”

Section: The Possibilities Of Geostatistical Methods In Real Estate Mmentioning

confidence: 99%

The Possibilities and Limitations of Geostatistical Methods in Real Estate Market Analyses

Cellmer

2014

Real Estate Management and Valuation

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In the traditional approach, geostatistical modeling involves analyses of the spatial structure of regionalized data, as well as estimations and simulations that rely on kriging methods. Geostatistical methods can complement traditional statistical models of property transaction prices, and when combined with those models, they offer a comprehensive tool for spatial analysis that is used in the process of developing land value maps. Transaction prices are characterized by mutual spatial correlations and can be considered as regionalized variables. They can also be regarded as random variables that have a local character and a specific probability distribution.This study explores the possibilities of applying geostatistical methods in spatial modeling of the prices of undeveloped land, as well as the limitations associated with those methods and the imperfect nature of the real estate market. The results are discussed based on examples, and they cover both the modeling process and the generated land value maps.

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The Dynamics of Location in Home Price

Cited by 99 publications

References 27 publications

Finding an Impact of Preservation Policies: Price Effects of Historic Landmarks on Attached Homes in Chicago, 1990-1999

Finding an Impact of Preservation Policies: Price Effects of Historic Landmarks on Attached Homes in Chicago, 1990-1999

Residential Real Estate, Risk, Return and Home Characteristics: Evidence from Sydney 2002-14

The Possibilities and Limitations of Geostatistical Methods in Real Estate Market Analyses

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