Take a Look Around

Abstract

Buying a home is one of the most important buying decisions people have to make in their life. Although it is rare that the complete transaction happens online, real estate websites play an increasingly prominent role in the overall decision-making process. For example, the online real estate company Zillow hosts a database of more than 110 million homes in the U.S. and had 196 million unique users in 2019 [7]. Besides search and recommendation capabilities, today's real estate websites offer various decision aids to support buyers as well as professional and non-professional sellers in estimating the price of a property. According to Zillow, its home valuation model Zestimate R uses statistical and machine learning models to predict the market value of a property and the prediction "is within 10% of the final sale price more than 95% of the time" [6].From a theoretical perspective, real estate evaluation models are based on hedonic pricing models introduced by Lancaster and Rosen in the 1970s and 80s [9,16]. The main idea of hedonic pricing models is that the overall price of a good can be estimated by decomposing it into its constituting characteristics, determining the value contribution of each individual characteristic, and summing these contributions up to obtain the final price. In the past, most researchers implemented hedonic real estate pricing models through linear regression models [11,8]. While linear models have the advantage of being transparent and interpretable, they typically suffer from high bias, as their assumptions, such as linearity and additivity, often do not align with reality [4].

Review on the Application of Artificial Neural Networks in Real Estate Valuation

“…This approach has also shown to be better in real estate evaluation than other notable offline and online methods [93]. The use of satellite images, street views and the accessibility in real estate price predictions have been reported [94].…”

Applying Comparable Sales Method to the Automated Estimation of Real Estate Prices

“…Currently, various machine learning and deep learning algorithms, such as Random Forest [17], Support Vector Machines (SVM) [18], and Long Short-Term Memory (LSTM) [19], have been used to predict real estate prices showing that the performance of real estate valuation can be improved by extending the predictor set to include mortgage contract rate [8], features extracted from home interiors and exteriors [20], or satellite images [21,22]. While some prior work focused on the estimation of the single property price, others applied the methods to predicting the trend of the real estate price index such as Zillow Home Value Index (ZHVI) [9].…”

URBAN-i: From urban scenes to mapping slums, transport modes, and pedestrians in cities using deep learning and computer vision

“…(2017) have used computer vision to measure the dynamics of neighbourhoods’ characteristics from time series street view images adjoined with socioeconomic data in five US cities. As a different approach, Law et al. (2018) used street view images to identify housing prices from urban perception relying on computer vision.…”