Modeling House Price Prediction using Regression Analysis and Particle Swarm Optimization Case Study : Malang, East Java, Indonesia

Abstract

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.

House Price Prediction using a Machine Learning Model: A Survey of Literature

“…In addition, the impact of various attributes on specific model have also been evaluated and debated. [38] Locational and structural attributes - [3] Locational and structural attributes 0.267 [39] Locational attributes - [40] Locational attributes - [10] Economic attributes - [41] Locational, structural and neighborhood attribute - [11] Locational and structural attributes 0.3079 [7] Copyright Based on reviewing numerous papers, there are several attributes used by researchers in their work to forecast house prices. All of these attributes can be divided into 4 main categories which are locational, structural, neighborhood and economic attributes.…”

“…Location is considered to be the most significant feature of house price determination [6,[9][10][11]. [12] in his study also observed the significant of location attributes in deciding house price.…”

“…Various considerations influence the price of a house. According to [10], the factors influencing house prices can be classified into three categories: location, structural and neighborhood condition.…”

“…Another significant feature influencing the house price is structural structure or some research has listed it as physical attributes [10,13]. Structural characteristic is a feature that people may identify, whether number of bedrooms and bathrooms, or floor space, or garage and patio.…”

“…The model selection to be used to predict house price is quite critical as varieties of models are available. One of the most commonly utilized models in this research field is Regression Analysis which is used in many studies, including [3,10,21]. Another common model for house price predictions is the Support Vector Regression (SVR) [7,22,23].…”