The welfare benefit of a home’s location: an empirical comparison of spatial and non-spatial model estimates

Koschinsky, Julia; Lozano‐Gracia, Nancy; Piras, Gianfranco

doi:10.1007/s10109-011-0148-6

Cited by 31 publications

(23 citation statements)

References 66 publications

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“…These two reasons may lead to a spatial component of land use data, which has the tendency to deviate from the often-assumed independence of observations [56]. From an econometric perspective, the incorporation of spatial dependence into the prediction of land use choices can improve model’s goodness-of-fit, reduce the probability of omitted variable bias, provide better model coefficient estimates and improve the analysis of marginal effects [57,58,59,60]. …”

Section: Literature Reviewmentioning

confidence: 99%

Proximal Association of Land Management Preferences: Evidence from Family Forest Owners

2017

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Individual behavior is influenced by factors intrinsic to the decision-maker but also associated with other individuals and their ownerships with such relationship intensified by geographic proximity. The land management literature is scarce in the spatially integrated analysis of biophysical and socio-economic data. Localized land management decisions are likely driven by spatially-explicit but often unobserved resource conditions, influenced by an individual’s own characteristics, proximal lands and fellow owners. This study examined stated choices over the management of family-owned forests as an example of a resource that captures strong pecuniary and non-pecuniary values with identifiable decision makers. An autoregressive model controlled for spatially autocorrelated willingness-to-harvest (WTH) responses using a sample of residential and absentee family forest owners from the U.S. State of Missouri. WTH responses were largely explained by affective, cognitive and experience variables including timber production objectives and past harvest experience. Demographic variables, including income and age, were associated with WTH and helped define socially-proximal groups. The group of closest identity was comprised of resident males over 55 years of age with annual income of at least $50,000. Spatially-explicit models showed that indirect impacts, capturing spillover associations, on average accounted for 14% of total marginal impacts among statistically significant explanatory variables. We argue that not all proximal family forest owners are equal and owners-in-absentia have discernible differences in WTH preferences with important implications for public policy and future research.

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Section: Literature Reviewmentioning

confidence: 99%

Proximal Association of Land Management Preferences: Evidence from Family Forest Owners

2017

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“…Spatial interactions in house or land markets have been well recognized in at least two ways. The first regards the actual workings of the real estate market, in which the sale prices of nearby properties are often used to guide the price of a property for sale (e.g., Can 1992;Koschinsky, Lozano-Gracia, and Piras 2012). The second is related to the interacting decision-making process like house maintenance or repairing decisions among householders (Can and Megbolugbe 1997).…”

Section: Application Of the Hsar Modelmentioning

confidence: 99%

Spatial Autoregressive Models for Geographically Hierarchical Data Structures

Dong

Harris

2014

Geographical Analysis

111

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This article discusses how standard spatial autoregressive models and their estimation can be extended to accommodate geographically hierarchical data structures. Whereas standard spatial econometric models normally operate at a single geographical scale, many geographical data sets are hierarchical in nature—for example, information about houses nested into data about the census tracts in which those houses are found. Here we outline four model specifications by combining different formulations of the spatial weight matrix W and of ways of modeling regional effects. These are (1) groupwise W and fixed regional effects; (2) groupwise W and random regional effects; (3) proximity‐based W and fixed regional effects; and (4) proximity‐based W and random regional effects. We discuss each of these model specifications and their associated estimation methods, giving particular attention to the fourth. We describe this as a hierarchical spatial autoregressive model. We view it as having the most potential to extend spatial econometrics to accommodate geographically hierarchical data structures and as offering the greatest coming together of spatial econometric and multilevel modeling approaches. Subsequently, we provide Bayesian Markov Chain Monte Carlo algorithms for implementing the model. We demonstrate its application using a two‐level land price data set where land parcels nest into districts in Beijing, China, finding significant spatial dependence at both the land parcel level and the district level.

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“…Physical factors including floor areas, a building's age, and the number of stories, bedrooms, and bathrooms, have proven to be significant to varying degrees [6][7][8][9][10][11][12]. Jun and Kim [12], for instance, found that both floor areas and the number of stories have positive impacts on the monthly rent charged for apartments; the age of a dwelling, however, influences rent negatively.…”

Section: Introductionmentioning

confidence: 99%

“…More recently, academics have investigated the effects of these attributes. They encompass access to green and open spaces, access to amenities such as parks, playgrounds, sea views, watercourses, and landscapes, and environmental conditions such as noise and air quality levels [6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Investigating How the Rents of Small Urban Houses are Determined: Using Spatial Hedonic Modeling for Urban Residential Housing in Seoul

Won

Lee

2017

Sustainability

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Abstract:The Seoul metropolitan government has launched the Urban Residential Housing (URH) program to address the shortage of small urban houses, and enhance residential stability for the increasing number of one-and two-person households in Seoul, Korea. While studies have examined the price premium of locational and neighborhood environmental features for houses in general, little is known about how and to what extent these features influence the rents of small urban houses. We estimate and compare conventional and spatial hedonic price models (HPMs) to examine the effects of those features on the monthly rents of URH units, while using geographic information system techniques to measure the variables. We found that the spatial HPM outperforms the conventional HPM in terms of goodness of fit measures. All of the locational features, and most of the variables with respect to the neighborhood environment, had a significant impact on rents. Rent tended to be lower in areas adjacent to a university, suggesting that it is a good place for one and two-person households to enhance residential affordability. However, access to parks does not appear to be important for residents. We conclude that the people who live in small urban houses are inclined to place a premium on rents in different ways.

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The welfare benefit of a home’s location: an empirical comparison of spatial and non-spatial model estimates

Cited by 31 publications

References 66 publications

Proximal Association of Land Management Preferences: Evidence from Family Forest Owners

Proximal Association of Land Management Preferences: Evidence from Family Forest Owners

Spatial Autoregressive Models for Geographically Hierarchical Data Structures

Investigating How the Rents of Small Urban Houses are Determined: Using Spatial Hedonic Modeling for Urban Residential Housing in Seoul

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