The Complex Neural Network Model for Mass Appraisal and Scenario Forecasting of the Urban Real Estate Market Value That Adapts Itself to Space and Time

Abstract

In the modern scientific literature, there are many reports about the successful application of neural network technologies for solving complex applied problems, in particular, for modeling the urban real estate market. There are neural network models that can perform mass assessment of real estate objects taking into account their construction and operational characteristics. However, these models are static because they do not take into account the changing economic situation over time. Therefore, they quickly become outdated and need frequent updates. In addition, if they are designed for a specific city, they are not suitable for other cities. On the other hand, there are several dynamic models taking into account the overall state of the economy and designed to predict and study the overall price situation in real estate markets. Such dynamic models are not intended for mass real estate appraisals. The aim of this article is to develop a methodology and create a complex model that has the properties of both static and dynamic models. Moreover, our comprehensive model should be suitable for evaluating real estate in many cities at once. This aim is achieved since our model is based on a neural network trained on examples considering both construction and operational characteristics, as well as geographical and environmental characteristics, along with time-changing macroeconomic parameters that describe the economic state of a specific region, country, and the world. A set of examples for training and testing the neural network were formed on the basis of statistical data of real estate markets in a number of Russian cities for the period from 2006 to 2020. Thus, many examples included the data relating to the periods of the economic calm for Russia, along with the periods of crisis, recovery, and growth of the Russian and global economy. Due to this, the model remains relevant with the changes of the international economic situation and it takes into account the specifics of regions. The model proved to be suitable for solving the following tasks: industrial economic analysis, company strategic and operational management, analytical and consulting support of investment, and construction activities of professional market participants. The model can also be used by government agencies authorized to conduct public cadastral assessment for calculating property taxes.

Rent index forecasting through neural networks

“…, 2021), Turkey (Selim, 2009; Kitapci et al. , 2017; Terregrossa and Ibadi, 2021), Nigeria (Igbinosa, 2011; Abidoye and Chan, 2017, 2018), Russia (Yasnitsky et al , 2021) and South Korea (Kang et al. , 2020).…”

“…the gross national product, gross domestic product, stock market index, consumer price index, default rate, interest rate and unemployment) for valuations (Kang et al. , 2020), from prices themselves for technical forecasts (Gu et al , 2011; Li et al , 2020; Xin and Runeson, 2004), from house-related characteristics for technical forecasts (Chen et al , 2017; Embaye et al , 2021; Igbinosa, 2011; Kang et al , 2020; Kitapci et al , 2017; Lam et al , 2008; Liu and Liu, 2019; Morano and Tajani, 2013; Nghiep and Al, 2001; Park and Bae, 2015; Rico-Juan and de La Paz, 2021; Terregrossa and Ibadi, 2021; Yasnitsky et al , 2021) and from macroeconomics for technical forecasts (Azadeh et al , 2014; Kang et al , 2020; Lam et al , 2008; Liu and Liu, 2019; Plakandaras et al , 2015; Rico-Juan and de La Paz, 2021; Xin and Runeson, 2004; Yasnitsky et al , 2021).…”

“…Some previous studies focus on using a specific machine learning model for price-related forecasting in the housing market (Abidoye and Chan, 2017; Azadeh et al , 2014; Chen et al , 2017; Chiarazzo et al , 2014; Gu et al , 2011; Igbinosa, 2011; Kang et al , 2020; Kitapci et al , 2017; Lam et al , 2008; Li et al , 2020; Morano et al , 2015; Xin and Runeson, 2004; Yasnitsky et al , 2021), some on making comparisons among different machine learning models (Park and Bae, 2015; Li et al. , 2017; Liu and Liu, 2019; Ho et al.…”

“…, 2016), including price series in the housing market (e.g. Abidoye and Chan, 2017, 2018; Azadeh et al , 2014; Chiarazzo et al , 2014; Igbinosa, 2011; Kang et al , 2020; Kitapci et al , 2017; Lam et al , 2008; Li et al , 2017; Morano et al , 2015; Morano and Tajani, 2013; Nghiep and Al, 2001; Peterson and Flanagan, 2009; Selim, 2009; Terregrossa and Ibadi, 2021; Xin and Runeson, 2004; Xu and Zhang, 2021g; Yasnitsky et al , 2021).…”

Deep learning-based modeling of second-hand ship prices in South Korea

“…Several studies have estimated ship prices [9], [19]- [21], and recently, NNs have begun to be utilized to enhance the predictive accuracy of real estate valuation [22]- [24] and ship valuation [10]. However, most studies have not employed categorical variables for valuation, and even the few studies that have used these variables have not explicitly exploited the advantages of the aforementioned entity embedding technique.…”