Hedonic Modeling in Real Estate Appraisal: The Case of Environmental Damages Assessment

Reichert, Alan K.

doi:10.1007/978-1-4615-0909-7_11

Cited by 1 publication

(1 citation statement)

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“…Timeseries models like autoregression [7] can reflect trends more adequately, and are often used to model supply and demand within a market [8]. Academic approaches to predicting home prices have traditionally relied on the hedonic approach, in which the sale price of a property is understood as a function of a finite set of characteristics [9]. However, hedonic models are restrictive, imposing uniformity of coefficients across both space and time [10], and time-series methods prove to be efficient only when inaccessible, proprietary data from eminent brokerage houses are employed.…”

Section: Related Workmentioning

confidence: 99%

What Image Features Boost Housing Market Predictions?

Kostić

Jevremović

2020

IEEE Trans. Multimedia

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The attractiveness of a property is one of the most interesting, yet challenging, categories to model. Image characteristics are used to describe certain attributes, and to examine the influence of visual factors on the price or timeframe of the listing. In this paper, we propose a set of techniques for the extraction of visual features for efficient numerical inclusion in modern-day predictive algorithms. We discuss techniques such as Shannon's entropy, calculating the center of gravity, employing image segmentation, and using Convolutional Neural Networks. After comparing these techniques as applied to a set of propertyrelated images (indoor, outdoor, and satellite), we conclude the following: (i) the entropy is the most efficient single-digit visual measure for housing price prediction; (ii) image segmentation is the most important visual feature for the prediction of housing lifespan; and (iii) deep image features can be used to quantify interior characteristics and contribute to captivation modeling. The set of 40 image features selected here carries a significant amount of predictive power and outperforms some of the strongest metadata predictors. Without any need to replace a human expert in a real-estate appraisal process, we conclude that the techniques presented in this paper can efficiently describe visible characteristics, thus introducing perceived attractiveness as a quantitative measure into the predictive modeling of housing.

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