Abstract
Real estate prices are an important indicator of the economic health of a region. The real estate industry is also growing at a very fast pace and needs the confluence of technology to provide knowledge-enabled services. We tried to explore the drivers of real estate housing. The present investigation is being conducted using a sample of 414 unique UCI datasets on real estate pricing. The OLS multivariate is being performed with the help of control variables such as house cost, age, MRT station, number of accommodation stores, walking and geographic directions. The article provides evidence that almost all the control variables are perceived to be crucial in predicting house price. However, the article did not provide any evidence to support that the geographic coordinate (in longitude) influence sample houses’ price. We argue the house cost is the most important determinate of real estate housing. Besides, ages, MRT station, the numbers of accommodation stores also help to improve the price of real estate housing. The finding of this study will provide investors and other stakeholders with important implications of real estate housing pricing in the best interests of capital appreciations.
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Mittal, R., Kumar, P., Mittal, A., Malik, V. (2021). Developing an Evaluation Model for Forecasting of Real Estate Prices. In: Choudhary, A., Agrawal, A.P., Logeswaran, R., Unhelkar, B. (eds) Applications of Artificial Intelligence and Machine Learning. Lecture Notes in Electrical Engineering, vol 778. Springer, Singapore. https://doi.org/10.1007/978-981-16-3067-5_46
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DOI: https://doi.org/10.1007/978-981-16-3067-5_46
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