Abstract
House prices increase every year, so there is a need for a system to predict house prices in the future. House price prediction can help the developer determine the selling price of a house and can help the customer to arrange the right time to purchase a house. There are three factors that influence the price of a house which include physical conditions, concept and location. This research aims to predict house prices based on NJOP houses in Malang city with regression analysis and particle swarm optimization (PSO). PSO is used for selection of affect variables and regression analysis is used to determine the optimal coefficient in prediction. The result from this research proved combination regression and PSO is suitable and get the minimum prediction error obtained which is IDR 14.186.
Highlights
Investment is a business activity that most people are interested in this globalization era
Based on preliminary research conducted, there are two standards of house price which are valid in buying and selling transaction of a house that is house price based on the developer and price based on Value of Selling Tax Object (NJOP)
Particle swarm optimization (PSO) is proposed to find the coefficients aimed at obtaining optimal results [8]
Summary
Investment is a business activity that most people are interested in this globalization era. Indonesian Central Bureau of Statistics states that in East Java 50% of the population of East Java classified as a young population who have age approximately at 30 years old [2] The result of this census indicates that the younger generation will need a house or buy a house in the future. Previous research conducted by Gharehchopogh, et al [7] using linear regression approach get 0,929 error with the actual price. Particle swarm optimization (PSO) is proposed to find the coefficients aimed at obtaining optimal results [8] Some previous researches such as Marini and Walzack [9], [10] show that PSO gets better results than other hybrid methods. This research aims to create a house price prediction model using regression and PSO to obtain optimal prediction results. This research is focused in Malang City, because Malang is one of tourism and urban city in East Java
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