The house price determination process: Rational expectations with a spatial context

Kiefer, Hua

doi:10.1016/j.jhe.2011.08.002

Cited by 22 publications

(17 citation statements)

References 18 publications

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“…Spatial heterogeneity, namely non-stationary dynamics across space, refers here to spatial variations in housing prices and household preferences [28,[45][46][47]. The existence of spatial heterogeneity makes the OLS-based hedonic price model (HPM) invalid, because of its stringent assumptions that the coefficients must be constant over all places, as well as its neglect of spatial effects [46,[48][49][50]. Alternatively, local spatial models such as geographically-weighted regression (GWR) have been widely employed to control spatial heterogeneity [28,51].…”

Section: Spatial Heterogeneity Urban Structure and Housing Pricesmentioning

confidence: 99%

Urban Structure, Subway Systemand Housing Price: Evidence from Beijing and Hangzhou, China

et al. 2019

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Using housing market data of Beijing and Hangzhou, China, we conduct a case study to detect how the difference of urban structure can affect the relationship between the subway system and housing prices. To quantify the characteristics of urban structure, we propose a constrained clustering method, which can not only reveal the spatial heterogeneity of the housing market, but also provides a link between heterogeneity and the underlying urban structure. Applying constrained clustering to Beijing and Hangzhou, we find that the relationship between accessibility to metro stations and housing prices is weak and vulnerable, while the improvement of commuting efficiency, measured by a key variable, the metro index, does have a robust connection to metro premium on housing units. In particular, only a large metro index can be associated with a positive metro premium. Structural features, such as the size of urban core and the existence of multiple sub-centers, influence the metro premium by affecting the value and spatial distribution of the metro index. The evidence from Beijing and Hangzhou supports that in a mono-centric city, the size of the urban core is positively associated with the metro index and the metro premium, while in a poly-centric city with a small urban core, the metro index tends to be lower in the core region and higher in the satellite regions, which enforces the metro premium to be negative in the core while positive outside of the core.

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Section: Spatial Heterogeneity Urban Structure and Housing Pricesmentioning

confidence: 99%

Urban Structure, Subway Systemand Housing Price: Evidence from Beijing and Hangzhou, China

et al. 2019

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“…The potential bias in using ADS data in models investigating the impact of natural disturbances on home prices was studied by Moeltner et al [30]. The authors recognized that two types of measurement error, erroneous damage assessment and imprecise geocoding, can lead to substantial biases in estimated losses of home values.…”

Section: Previous Researchmentioning

confidence: 99%

“…In hedonic analyses, previous studies have shown that expected future changes in neighborhood characteristics can influence current property sale prices, representing "forward-looking behavior" [30][31][32][33]. Ignoring the forward-looking behavior of housing markets may lead to a misspecification of the hedonic model, and biased estimates of the marginal price for relevant time-varying attribute variables.…”

Section: Previous Researchmentioning

confidence: 99%

Hedonic Analysis of Forest Pest Invasion: the Case of Emerald Ash Borer

Holmes

Boyle

et al. 2019

Forests

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The emerald ash borer (EAB) was first detected in North America in 2002, and since its introduction, this invasive pest has killed millions of ash trees. While EAB kills native North American ash trees in all settings, its impacts have been especially large in urban areas where ash has been a dominant street tree, especially in residential areas. While some management costs, such as insecticide treatment, tree removal, or tree replacement, are relatively straightforward to compute, the impact that EAB has had on residential property values is less clear. To better understand the economic cost of EAB in urban settings, we conducted a hedonic property value analysis to evaluate the impact of ash tree damages due to EAB infestation on housing sales prices. This study was conducted in the city of Milwaukee, Wisconsin, which had high stocking levels of ash trees prior to EAB’s arrival. The objectives of the study are to investigate: (1) how EAB-infested ash trees affect property values; (2) whether the benefits from healthy ash trees to property value change after arrival of EAB; and (3) whether healthy ash trees located within infested neighborhoods provide the same benefits as the healthy ash trees located outside of infested neighborhoods. In general, our results show that the EAB outbreak has had a negative impact on home values for properties located in close proximity to the ash tree component of the urban forest. This result holds true for neighborhoods where EAB does not yet pose an imminent threat, and is amplified for neighborhoods where EAB has been detected. Our results highlight the early stages of a dynamic economic process that impacts urban residential property owners subject to the risk of EAB or other tree pests and diseases. In general, we find that forward-looking behavior of residential property owners is capitalized into property values during the process of forest pest infestation.

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“…Çoklu regresyon tekniklerini kullanan hedonik modeller konut fiyatlarının belirlenmesinde sıklıkla kullanılmaktadır (Coulson ve McMillen, 2008;Fletcher vd., 2004;Kiefer, 2011;Kiel ve Zabel, 2008;Stadelmann, 2010). Hedonik modelle, bir malın özelliklerinin fiyat üzerindeki etkisi araştırılır.…”

Section: şEkil 1 Türkiye'deki Konut Satış Sayıları Kaynak: Tüi̇kunclassified

“…Satın alınması düşünülen konutlar konutun bulunduğu mevki, yapısal özellikleri, yakın çevresi ve kamu hizmetinden yararlanabilme olanakları göz önünde bulundurularak değerlendirilir (Kain ve Quigley, 1975). Şehir merkezine yakınlık, ulaşım olanaklarının olması ve çeşitliliği, düşük vergiler, kamu hizmetlerinin kalitesi, eğitim kurumlarına yakınlık, çevrenin sosyo-ekonomik özellikleri, doğa güzellikleri gibi etmenlerin konut fiyatını arttıran özellikler olması beklenebilir (Kiefer, 2011;Stadelmann, 2010). Maclennan (1977), hedonik modelin uygulamalarını ele aldığı çalışmasında konut fiyatını belirleyen etmenleri araştırmış ve özellikle çevresel faktörlerin fiyat üzerindeki etkisini belirlemeye çalışmıştır.…”

Section: Literatür Araştırmasıunclassified

Comparision of Hedonic Regression Method and Artificial Neural Networks to Predict Housing Prices in Turkey

Ecer

2014

International Conference on Eurasian Economies 2014

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Owner-occupied housing is both a place to live and also the most important asset in many households’ portfolio. Accurately predicting of house prices is therefore of great interest to the general public. This paper aims to compare the housing price prediction accuracies of Hedonic Model (HM) and Artificial Neural Networks (ANNs). In order to achieve this aim, two techniques’ prediction results were compared by using four performance criteria: RMSE, MAE, MAD, and Theil’s U statistic. This study uses the HM and ANNs to empirically determine the house prices in Turkey. HM is the standard technique for modeling the behavior of house prices over the past three decades and is based on micro economic theory. The non-linear relationship between house price and its determinants can be modeled by an ANN, so it is employed in this paper as an alternative method. Empirical results revealed that ANNs performed better than HM in house price predictions, indicating that ANNs could be useful for prediction of house prices. More clearly, the performance criteria from the ANNs are smaller than those from the HM by roughly 60-90%. For instance, the ANN model has about 77 percent lower RMSE, 91 percent lower MAE, 64 percent lower MAD, and 77 percent lower Theil’s U statistic than those of the HM.

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The house price determination process: Rational expectations with a spatial context

Cited by 22 publications

References 18 publications

Urban Structure, Subway Systemand Housing Price: Evidence from Beijing and Hangzhou, China

Urban Structure, Subway Systemand Housing Price: Evidence from Beijing and Hangzhou, China

Hedonic Analysis of Forest Pest Invasion: the Case of Emerald Ash Borer

Comparision of Hedonic Regression Method and Artificial Neural Networks to Predict Housing Prices in Turkey

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