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RISS 인기검색어
- 검색키워드
- 저자 : Mankad Mudit D. (검색결과 2 건)
- 무료
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Comparing OLS based hedonic model and ANN in house price estimation using relative location
Mankad Mudit D. 대한공간정보학회 2022 Spatial Information Research Vol.30 No.2
Accurate house price estimation is of great importance not only to various real estates stakeholders such as house owners, buyers, investors, and agents but also to banking and insurance sectors among others. This estimation is a very challenging and complex task as it involves a variety of attributes. Among all attributes, location is one of the most influential attributes affecting house price. The present study has incorporated selected structural attributes and relative location through spatial attributes (geographical, infrastructural, and neighborhood) for modeling the influence of location on house prices. The hedonic model, a traditional method for estimating house price has been criticized due to nonlinearity, multicollinearity, and heteroskedasticity problems. Unlike the hedonic model, the Artificial Neural Network (ANN) permits nonlinear relationships and also tries to solve the problem of multicollinearity. The present study aims to examine the application of ANN in estimating accurate house prices and comparing the results with the Ordinary Least Squares (OLS) based hedonic model. The Gotri area located in the western part of Vadodara city, India is considered as a case study. TensorFlow (a python environment) of Google is used to implement the ANN model. In ANN model building, a three-layer network with a single hidden layer and RELU (REctified Linear Unit) activation function is adopted. The estimation performance has been evaluated by employing Root Mean Squared Error and Mean Absolute Percentage Error. According to results, ANN was found better when compared to OLS in terms of both the performance measures. This paper suggests that the ANN estimator could be a complement to the OLS based linear regression method.
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Comparing OLS based hedonic model and ANN in house price estimation using relative location
Mankad Mudit D. 대한공간정보학회 2022 Spatial Information Research Vol.30 No.1
Accurate house price estimation is of great importance not only to various real estates stakeholders such as house owners, buyers, investors, and agents but also to banking and insurance sectors among others. This estimation is a very challenging and complex task as it involves a variety of attributes. Among all attributes, location is one of the most influential attributes affecting house price. The present study has incorporated selected structural attributes and relative location through spatial attributes (geographical, infrastructural, and neighborhood) for modeling the influence of location on house prices. The hedonic model, a traditional method for estimating house price has been criticized due to nonlinearity, multicollinearity, and heteroskedasticity problems. Unlike the hedonic model, the Artificial Neural Network (ANN) permits nonlinear relationships and also tries to solve the problem of multicollinearity. The present study aims to examine the application of ANN in estimating accurate house prices and comparing the results with the Ordinary Least Squares (OLS) based hedonic model. The Gotri area located in the western part of Vadodara city, India is considered as a case study. TensorFlow (a python environment) of Google is used to implement the ANN model. In ANN model building, a three-layer network with a single hidden layer and RELU (REctified Linear Unit) activation function is adopted. The estimation performance has been evaluated by employing Root Mean Squared Error and Mean Absolute Percentage Error. According to results, ANN was found better when compared to OLS in terms of both the performance measures. This paper suggests that the ANN estimator could be a complement to the OLS based linear regression method.
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