Forecasting house prices in Iran using GMDH

Author:

Nazemi Behrooz,Rafiean Mohsen

Abstract

Purpose An accurate predictive model for forecasting urban housing price in Isfahan can be useful for sellers and owners to take more appropriate actions about housing supplying. Also, it can help urban housing planners and policymakers in managing of the housing market and preventing an urban housing crisis in Isfahan. The purpose of this paper is forecasting housing price in Isfahan city of Iran until 2022 using group method of data handling (GMDH). Design/methodology/approach This paper presents an accurate predictive model by applying the GMDH algorithm by using GMDH-Shell software for forecasting housing price in municipal boroughs of Isfahan city till the second half of 2022 based on creating time series and existing data. Alongside housing price, some other affecting factors have been also considered to control the forecasting process and make it more accurate. Furthermore, this research shows the housing price changes of boroughs on map using ArcMap. Findings Based on forecasting results, the housing price will increase at all boroughs of Isfahan till second half of the year 2022. Amongst them, Borough 15 will have the highest percentage of the price increasing (28.27%) to year 2022 and Borough 6 will have the lowest percentage of the price increasing (8.34%) to the year 2022. About ranking of the boroughs in terms of housing price, Borough number 6 and 3 will keep their current position at the top and Borough number 15 will stay at the bottom. Research limitations/implications In this research, just few factors have been selected alongside housing price to control the forecasting process owing to limitation of reliable data availability about affecting factors. Originality/value The most remarkable point of this paper is reaching to a mathematical formula that can accurately forecast housing price in Isfahan city which has been rarely investigated in former studies, especially in simplified form. The technique used in this paper to forecast housing price in Isfahan city of Iran can be useful for other cities too.

Publisher

Emerald

Subject

General Economics, Econometrics and Finance

Reference46 articles.

1. Using GMDH neural networks to model the power and torque of a stirling engine;Sustainability,2015

2. Applying GMDH neural network to estimate the thermal resistance and thermal conductivity of pulsating heat pipes;Engineering Applications of Computational Fluid Mechanics,2019

3. Using ridge regression with genetic algorithm to enhance real estate appraisal forecasting;Expert Systems with Applications,2012

4. Modeling house price prediction using regression analysis and particle swarm optimization,2017

5. Investigation of smart growth in traditional islamic culture: case study of isfahan city in Iran;Journal of Geography and Regional Planning,2017

Cited by 25 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Short-Term Wind Speed Prediction Using GMDH Neural Networks: Binalood Wind Farm Case Study;2023 13th Smart Grid Conference (SGC);2023-12-05

2. Exploration of House Price Prediction Based on ICS-BP Neural Network;2023 4th International Conference on Machine Learning and Computer Application;2023-10-27

3. Comparing simple and complex regression models in forecasting housing price: case study from Kenya;International Journal of Housing Markets and Analysis;2023-07-05

4. Embedded Learning Approaches in the Whale Optimizer to Solve Coverage Combinatorial Problems;Mathematics;2022-11-30

5. Prediction on Housing Price Based on the Data on Kaggle;Proceedings of the 2022 3rd International Conference on E-commerce and Internet Technology (ECIT 2022);2022-11-10

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3