Predicting House Prices with Spatial Dependence: A Comparison of Alternative Methods

Bourassa, Steven C.; Cantoni, Eva; Hoesli, Martin

doi:10.1080/10835547.2010.12091276

Cited by 168 publications

(77 citation statements)

References 22 publications

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“…In particular, the variables were selected on the basis of (i) the indications given by the market operators in the area, (ii) the data generated by OMI and (iii) research found in the reference literature [51][52][53][54]. In fact, several scientific papers pointed out the unavoidable tradeoff between bias from omitted factors and increased sampling variance related to the collinearity that is involved in this step [55], even if a relative agreement on the major influencing factors is observed [56][57][58].…”

Section: Variablesmentioning

confidence: 99%

Economic Evaluation of the Indoor Environmental Quality of Buildings: The Noise Pollution Effects on Housing Prices in the City of Bari (Italy)

et al. 2021

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Among environmental factors, noise represents one of the most relevant determinants on human health and on the urban quality level and, consequently, on real estate values. Thus, the noise pollution issue plays a significant role in public urban policies aimed at increasing the acoustic comfort level and creating more sustainable and comfortable cities. The real estate market is highly sensitive to noise factor and the residential prices can be strongly influenced by a high acoustic pollution rate. The present research aims to analyze the functional relationships between noise pollution and selling prices in four municipal areas of the city of Bari (Southern Italy). For each area, a study sample constituted by two hundred residential properties sold in 2017–2019 was detected for the identification of the main influential factors on prices and the investigation of the contribution of noise on them. The implementation of an econometric technique was used to obtain four different models (one for each municipal area of the city of Bari) able to explain the specific impact of noise pollution level on selling prices. From the comparison of the results obtained for each area, the outputs confirm the expected phenomena in terms of a decrease of noise component influence on residential prices from the central area to the peripheral. For the suburban area of the city of Bari, the model obtained does not include the noise pollution factor, showing a lower (scarce) importance of the environmental factor among the buyer and seller bargaining phases.

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Section: Variablesmentioning

confidence: 99%

Economic Evaluation of the Indoor Environmental Quality of Buildings: The Noise Pollution Effects on Housing Prices in the City of Bari (Italy)

et al. 2021

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“…Upon inspection of these data points, no clear outliers are evident, indicating that these points may have high degrees of leverage or influence on the model. Bourassa et al (2010) found that property price predictions were more accurate when submarket dummy locational variables were used and the presence of spatial autocorrelation was mitigated. In order to formally test for the presence of spatial autocorrelation the Mantel test was computed.…”

Section: Variance Inflation Factormentioning

confidence: 98%

“…Broadly speaking, spatial autocorrelation can be defined as the dependence of observations across geographic locations, which has the propensity to render the standard errors of ordinary least squares models inefficient and biased (Liao & Wang 2012). Bourassa et al (2010) found that property price predictions were more accurate when submarket dummy locational variables were used in contrast to using traditional statistical methods alone; however, incorporating both methods yielded the best results. Notably they argue that the use of submarket dummy locational variables in ordinary least squares is a far simpler technique than trying to model the structure of the errors using complicated statistical methods, and the benefit was evident in their results.…”

Section: Residential Hedonic Modelsmentioning

confidence: 99%

Listing price estimation of apartments: A generalised linear model

Bax

Chasomeris

2019

Journal of Economic and Financial Sciences

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Orientation: Residential property is an important segment of the property market in South Africa. Residential property transactions are typically infrequent and relate to a highly differentiated set of items making measurement techniques complex and difficult. Research purpose:The aim of this research was to develop a statistical model to estimate listing prices of apartments in KwaZulu-Natal, South Africa, and build a software application to disseminate the results thereof.Motivation for the study: This study presents a novel alternative to the log linear (ordinary least squares) method of deriving a hedonic price function for residential property where the arithmetic mean is computed as the expected value and not the geometric mean.Research design, approach and method: Using a data set of 1314 residential apartments provided by Private Property (Pty) Ltd, this research derives a hedonic price function for residential property using a generalised linear model based on the gamma distribution and log-link function. Main findings:The results showed that floor area, number of bedrooms, number of bathrooms and a dummy variable for suburb (location) were statistically significant determinants of listing prices.Practical/managerial implications: A software application, called the listing price calculator, was developed to disseminate the results of the model for commercial use by real estate buyers, sellers and agents, bridging the gap between academia and business. Contribution/value-add:This study derives a hedonic price function for residential property using a generalised linear model based on the gamma distribution and log-link function, which is novel in South African research.

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“…In many works the impact of location on prices is empirically demonstrated [30]. Contextually, the importance of geographical segmentation in price prediction is underlined in many researches [31][32][33][34][35].…”

Section: Analysis Of Pricing In Housing Economicsmentioning

confidence: 99%

“…The pilot studies have been followed in the last decades by a number of publications worldwide, oriented towards the treatment and modeling of the spatial effects. In fact, the well-known criticalities due to the spatial heterogeneity (heteroskedasticity) and the spatial autocorrelation (interdependence) are explored [32], with the support of tests for the detection of spatial effects [36][37][38], opening the way to the Spatial Econometrics. Implicitly, the recent spreading in spatial analyses is related to the weight caught by the location variable in values formation.…”

Section: Analysis Of Pricing In Housing Economicsmentioning

confidence: 99%

EPC Labels and Building Features: Spatial Implications over Housing Prices

2021

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The influence of building or dwelling energy performance on the real estate market dynamics and pricing processes is deeply explored, due to the fact that energy efficiency improvement is one of the fundamental reasons for retrofitting the existing housing stock. Nevertheless, the joint effect produced by the building energy performance and the architectural, typological, and physical-technical attributes seems poorly studied. Thus, the aim of this work is to investigate the influence of both energy performance and diverse features on property prices, by performing spatial analyses on a sample of housing properties listed on Turin’s real estate market and on different sub-samples. In particular, Exploratory Spatial Data Analyses (ESDA) statistics, standard hedonic price models (Ordinary Least Squares—OLS) and Spatial Error Models (SEM) are firstly applied on the whole data sample, and then on three different sub-samples: two territorial clusters and a sub-sample representative of the most energy inefficient buildings constructed between 1946 and 1990. Results demonstrate that Energy Performance Certificate (EPC) labels are gaining power in influencing price variations, contrary to the empirical evidence that emerged in some previous studies. Furthermore, the presence of the spatial effects reveals that the impact of energy attributes changes in different sub-markets and thus has to be spatially analysed.

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Predicting House Prices with Spatial Dependence: A Comparison of Alternative Methods

Cited by 168 publications

References 22 publications

Economic Evaluation of the Indoor Environmental Quality of Buildings: The Noise Pollution Effects on Housing Prices in the City of Bari (Italy)

Economic Evaluation of the Indoor Environmental Quality of Buildings: The Noise Pollution Effects on Housing Prices in the City of Bari (Italy)

Listing price estimation of apartments: A generalised linear model

EPC Labels and Building Features: Spatial Implications over Housing Prices

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