PREDICTING HOUSING SALES IN TURKEY USING ARIMA, LSTM AND HYBRID MODELS

Ayşe Soy Temür,Günay Temür,Melek Akgün

doi:10.3846/jbem.2019.10190

Ayşe Soy Temür, Günay Temür + Show 1 more

Open Access

https://doi.org/10.3846/jbem.2019.10190

Copy DOI

Abstract

Having forecast of real estate sales done correctly is very important for balancing supply and demand in the housing market. However, it is very difficult for housing companies or real estate professionals to determine how many houses they will sell next year. Although this does not mean that a prediction plan cannot be created, the studies conducted both in Turkey and different countries about the housing sector are focused more on estimating housing prices. Especially the developing technological advances allow making estimations in many areas. That is why the purpose of this study is both to provide guiding information to the companies in the sector and to contribute to the literature. In this study, a 124-month data set belonging to the 2008 (1) - 2018 (4) period has been taken into account for total housing sales in Turkey. In order to estimate the time series of sales, ARIMA (Auto Regressive Integrated Moving Average as linear model), LSTM (Long Short-Term Memory as nonlinear model) has been used. As to increase the estimation, a HYBRID (LSTM and ARIMA) model created has been used in the application. When MAPE (Mean Absolute Percentage Error) and MSE (Mean Squared Error) values obtained from each of these methods were compared, the best performance with the lowest error rate proved to be the HYBRID model, and the fact that all the application models have very close results shows the success of predictability. This is an indication that our study will contribute significantly to the literature.

Highlights

In order for supply and demand in the housing market to be balanced, it is very important that housing companies and real estate professionals determine how many houses will be sold in the year
The main goal of this study is to provide reliable and accurate estimation studies in the housing sector by using methods with a scientific basis and to ensure that housing sales are estimated as close as possible to the real value
Considering the Mean Squared Error (MSE) values, the hybrid model achieved a performance increase of 43% compared to the predictions made with the ARIMA model and a 49% performance increase compared to the predictions made with the Long Short-Term Memory (LSTM) model

Summary

Introduction

In order for supply and demand in the housing market to be balanced, it is very important that housing companies and real estate professionals determine how many houses will be sold in the year. The need for housing (shelter) is one of the basic needs along with physi-. Due to the housing sales, for many modern economies the construction sector is the catalyst of the economy and is seen among economic growth figures. New housing supply creates an economic wave effect such as homeowners buying goods like household appliances or furniture for their own homes, builders buying raw materials to meet the demands and leasing more employees. The most important external resource that makes housing investment easier in a certain term are bank loans and their interest rates. The interest variable is critical for sales and all the parameters that depend on it

Objectives

Methods

Findings

Discussion

Conclusion