Possibilities of House Valuation Automation in the Czech Republic

Endel, Stanislav; Teichmann, Marek; Kutá, Dagmar

doi:10.3390/su12187774

Cited by 7 publications

(8 citation statements)

References 36 publications

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“…Additionally, it helps to understand the relationships among the technical condition determinants and their correct use. The preference for selling prices makes this different from other works focused on price-setting factors (Özsoy and S ahin, 2022;Gordon and Winkler, 2016;Khezr, 2015;Hui and Yu, 2012;Endel et al, 2020).…”

Section: H2mentioning

confidence: 83%

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Technical condition of houses: a framework for the Czech market

Jandásková

Hrdlička

Cupal

et al. 2022

IJHMA

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Purpose This study aims to provide a framework for assessing the technical condition of a house to determine its market value, including the identification of other price-setting factors and their statistical significance. Time on market (TOM) in relation to the technical condition of a house is also addressed. Design/methodology/approach The primary database contains 631 houses, and the initial asking price and selling price are examined. All the houses are located in the Brno–venkov district in the Czech Republic. Regression analysis was used to test the influence of price-setting factors. The standard ordinary least squares estimator and the maximum likelihood estimator were used in the frame of generalized linear models. Findings Using envelope components of houses separately, such as the façade condition, windows, roof, condition of interior and year of construction, brings better results than using a single factor for the technical condition. TOM was found to be 67 days lower for houses intended for demolition – as compared to new houses – and 18 days lower for houses to refurbishment. Originality/value To the best of the authors’ knowledge, this paper is original in the substitution of specific price-setting factors for factors relating to the technical condition of houses as well as in proposing the framework for professionals in the Czech Republic.

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Section: H2mentioning

confidence: 83%

“…In the context of market value, houses are very specific assets representatives of the real estate sector, unlike classic commercial products (Endel et al , 2020; Wickramaarachchi et al , 2021), and form a heterogeneous market (Nam, 2020; Cupal, 2015).…”

Section: Introductionmentioning

confidence: 99%

Technical condition of houses: a framework for the Czech market

Jandásková

Hrdlička

Cupal

et al. 2022

IJHMA

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“…Assessments of residential property are generally considered as tasks that can be performed with a relatively high level of confidence. This is one of the reasons that there have been a significant number of studies on residential property valuation reported in the literature (Iii et al, 2002;Frew and Jud, 2003;Endel et al, 2020). In contrast, licensed appraisers and valuation modeling experts seem to accept that valuation of (vacant) land is a more delicate assignment (Demetriou, 2018).…”

Section: Property Valuationmentioning

confidence: 99%

“…This is one of the reasons that there have been a significant number of studies on residential property valuation reported in the literature (Iii et al. , 2002; Frew and Jud, 2003; Endel et al. , 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Designing an optimal neural network architecture: an application to property valuation

Lee

2022

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PurposeThe success of a neural network depends on, among others, an architecture that is appropriate for the task at hand. This study attempts to identify an optimal architecture of a neural network in the context of property valuation, and aims to test the ability of connecting related neural networks to reduce the property valuation error.Design/methodology/approachThis study explores efficient network architectures to estimate land and house prices in Seoul, South Korea. The input is structured data, and the embedding technique is used to process high-cardinality categorical variables.FindingsThe shared architecture of a network for simultaneous estimation of both land and houses was revealed to be the best performing network. Through weight sharing between relevant layers in networks, the root-mean-square error (RMSE) for land price estimation was reduced significantly, from 0.55–0.68 using the baseline architecture, to 0.44–0.47 using the shared architecture.Originality/valueThe study results are expected to encourage active investigation of efficient architectures by using domain knowledge, and to promote interest in using structured data, which is still the dominant type in most industries.

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“…It is convenient to process structured data using numerical models. The most common input data for house valuation include the number of rooms, floor area and year of build (Endel et al , 2020), all of which are categorized as structured data. In contrast, unstructured data such as images and free-form texts are difficult-to-process variables that cannot be directly utilized in valuation.…”

Section: Introductionmentioning

confidence: 99%

Measuring land lot shapes for property valuation

Lee

2023

DTA

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PurposeUnstructured data such as images have defied usage in property valuation for a long time. Instead, structured data in tabular format are commonly employed to estimate property prices. This study attempts to quantify the shape of land lots and uses the resultant output as an input variable for subsequent land valuation models.Design/methodology/approachImagery data containing land lot shapes are fed into a convolutional neural network, and the shape of land lots is classified into two categories, regular and irregular-shaped. Then, the intermediate output (regularity score) is utilized in four downstream models to estimate land prices: random forest, gradient boosting, support vector machine and regression models.FindingsQuantification of the land lot shapes and their exploitation in valuation led to an improvement in the predictive accuracy for all subsequent models.Originality/valueThe study findings are expected to promote the adoption of elusive price determinants such as the shape of a land lot, appearance of a house and the landscape of a neighborhood in property appraisal practices.

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Possibilities of House Valuation Automation in the Czech Republic

Cited by 7 publications

References 36 publications

Technical condition of houses: a framework for the Czech market

Technical condition of houses: a framework for the Czech market

Designing an optimal neural network architecture: an application to property valuation

Measuring land lot shapes for property valuation

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