Hedonic Regression Analysis in Real Estate Markets: A Primer

Sopranzetti, Ben J.

doi:10.1007/978-0-387-77117-5_78

Cited by 19 publications

(15 citation statements)

References 23 publications

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“…We also investigated the effect of different distance matrices on the predictions of GWR and GWANN and evaluated the spatial distribution of the residuals. We chose housing as a case study because in real-estate economics, regression-based house price assessments are vital (Sopranzetti 2010). Hedonic theory assumes that a property represents a heterogeneous good that can be decomposed into its utility-bearing characteristics, and that the resulting benefit is reflected in the property price (Rosen 1974).…”

Section: Experiments 2: House Prices In Austriamentioning

confidence: 99%

A geographically weighted artificial neural network

Hagenauer

Helbich

2021

International Journal of Geographical Information Science

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While recent developments have extended geographically weighted regression (GWR) in many directions, it is usually assumed that the relationships between the dependent and the independent variables are linear. In practice, however, it is often the case that variables are nonlinearly associated. To address this issue, we propose a geographically weighted artificial neural network (GWANN). GWANN combines geographical weighting with artificial neural networks, which are able to learn complex nonlinear relationships in a data-driven manner without assumptions. Using synthetic data with known spatial characteristics and a real-world case study, we compared GWANN with GWR. While the results for the synthetic data show that GWANN performs better than GWR when the relationships within the data are nonlinear and their spatial variance is high, the results based on the real-world data demonstrate that the performance of GWANN can also be superior in a practical setting.

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Section: Experiments 2: House Prices In Austriamentioning

confidence: 99%

A geographically weighted artificial neural network

Hagenauer

Helbich

2021

International Journal of Geographical Information Science

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“…Three types of function forms: linear, logarithmic and logarithmic-linear are often used in empirical analyses which employ the hedonic regression models [47]. Several researchers prefer to use the so-called semi-logarithmic model for different reasons [42,48]. One reason given is that the semi-logarithmic model coefficients can be interpreted as the percentage change of the independent variable [49,50].…”

Section: Methodology Data Collecting and Analysismentioning

confidence: 99%

Geo-Analysis of Compatibility Determinants for Data in the Land and Property Register (LPR)

Kocur-Bera

Stachelek

2019

Geosciences

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The development of modern technologies and accessibility of data on space and the natural environment has led to their increasing use for socio-economic purposes. Data users believe that these systems reflect the reality in the field. This applies in particular to databases used for construction investment projects or as the basis for calculations of financial obligations, e.g., taxes. The Land and Property Register (LPR), which is part of the Land Administration System, serves a number of economic and legal purposes. This geo-system often contains low-quality information regarding the technical potential of modern data acquisition methods and is continuously updated. The authors propose a two-step analysis of data contained in the LPR. The first step identified the sources of discrepancies between data from the LPR and the reality in the field. The second step emphasises the importance of the factors under analysis, which include both a plot’s geometric parameters, the geo-location features (associated with the natural environment elements) and factors associated with the supplementary data acquisition methods. The results show that sufficient quality data play the main role in achieving compatibility between the data in the Land and Property Register and with reality. Studies conducted so far have dealt with data on a global scale and were based on in situ data and focused on the specific values of each plot under analysis.

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“…The hedonic modelling techniques stem from works by Court (1939), Rosen (1974) and Sopranzetti (2010). The basic hedonic panel data model estimated in this paper had the following general form:…”

Section: Methodsmentioning

confidence: 99%

Is energy performance capitalized in office building appraisals?

Bonde

Song

2013

Property Management

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Purpose -Since 2009, all commercial buildings in Sweden should have undergone an energy performance rating in accordance with the European Union directive on the Energy Performance of Buildings. The main purpose of this rating is to illustrate a building's energy performance in an easy, straightforward, manner. In doing so, it becomes easier for the actors on the real estate market to assess the building's energy performance, which in the end should be reflected in the capital value of the property. The aim of this paper is to study the EU Energy Performance Certificates impact on office buildings' capital values.Design/methodology/approach -In this paper an econometric approach is used to estimate the energy performance impact on buildings' capital values. A panel data set was constructed using economic data from IPD Nordic and Energy Performance Certificates from the Swedish National Board of Housing, Building and Planning.Findings -This study shows that a building's energy performance has no impact on its capital value.

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Hedonic Regression Analysis in Real Estate Markets: A Primer

Cited by 19 publications

References 23 publications

A geographically weighted artificial neural network

A geographically weighted artificial neural network

Geo-Analysis of Compatibility Determinants for Data in the Land and Property Register (LPR)

Is energy performance capitalized in office building appraisals?

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