Distance in Geographic and Characteristics Space for Real Estate Price Prediction

Ozhegov, Evgeniy M.; Ozhegova, Alina; Mitrokhina, Ekaterina

doi:10.1007/978-3-030-37334-4_6

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…The research methodology is based on a SAR model where distance-weighted prices of other marketed objects are included to explain the price. We extend the current SAR framework by using the distance matrix measure initially proposed in Ozhegov et al (2019). Using the described dataset, we test whether the proximity in characteristics space used to estimate the spatially lagged price is a significant factor in explaining apartment prices.…”

Section: Introductionmentioning

confidence: 99%

Distance in geographic and characteristics space for real estate pricing

Ozhegov

Ozhegova

2021

IJHMA

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PurposeA common approach to predicting the price of residential properties uses the hedonic price model and its spatial extensions. Within the hedonic approach, real estate prices are decomposed into internal characteristics of an apartment, apartment characteristics and external characteristics. To account for the unobserved quality of the surrounding environment, price models include spatial price correlation factors, where the distance is usually measured as the distance in geographic space. In determining the price, a seller focuses not only on the observed and unobserved factors of the apartment and its environment but also on the prices of similar marketed objects that can be selected both by geographic proximity and by characteristics similarity. The purpose of this study is to show the latter point empirically. Design/methodology/approachThis study uses an ensemble clustering approach to measure objects' proximity and test whether the proximity of objects in the property characteristics space along with spatial correlation explain the significant variation in prices. FindingsIn this paper, the pricing behaviour of sellers in a reselling market in Perm, Russia is studied. This study shows that the price transmission mechanism includes both geographic and characteristics spaces. Practical implicationsAfter testing on market data, the proposed framework for the distance construct could be used to obtain higher predictive power for price predictive models and construction of automated valuation services. Originality/valueThis study tests the higher explanatory power of the model that includes both the distance measured in geographic and property characteristics spaces.

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Section: Introductionmentioning

confidence: 99%

Distance in geographic and characteristics space for real estate pricing

Ozhegov

Ozhegova

2021

IJHMA

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Cluster analysis for modeling the cost of industrial land objects in Krasnoyarsk region

Panfilov

Alekseeva

Shiryaeva

et al. 2020

J. Phys.: Conf. Ser.

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An assessment activity must have a legal justification. The regulatory documents are results of a land plot state assessment carried out in the Russia regions on a regular basis. In this research mathematical models for evaluating land plots in Krasnoyarsk region based on data from the state assessment of 2012 and 2017 were built. The models were supposed to formalize the mechanism for evaluating the state contractor when evaluating industrial land. Factor and cluster analysis methods were used. The factors were determined to a greater extent determining the change in the value of the objects of assessment. The data for the analysis was taken from open official sources. The simulation results showed unstable dependencies in the official results of the state contractor evaluation. Further analysis revealed errors in the official report.

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Distance in Geographic and Characteristics Space for Real Estate Price Prediction

Cited by 2 publications

References 15 publications

Distance in geographic and characteristics space for real estate pricing

Distance in geographic and characteristics space for real estate pricing

Cluster analysis for modeling the cost of industrial land objects in Krasnoyarsk region

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