Prediction of House Price Index Based on Bagging Integrated WOA-SVR Model

Aiming at the shortcomings of a single machine learning model with low model prediction accuracy and insufficient generalization ability in house price index prediction, a whale algorithm optimized support vector regression model based on bagging ensemble learning method is proposed. Firstly, gray correlation analysis is used to obtain the main influencing factors of house prices, and the segmentation forecasting method is used to divide the data set and forecast the house prices in the coming year using the data of the past ten years. Secondly, the whale optimization algorithm is used to find the optimal parameters of the penalty factor and kernel function in the SVR model, and then, the WOA-SVR model is established. Finally, in order to further improve the model generalization capability, a bagging integration strategy is used to further integrate and optimize the WOA-SVR model. The experiments are conducted to forecast the house price indices of four regions, Beijing, Shanghai, Tianjin, and Chongqing, respectively, and the results show that the prediction accuracy of the proposed integrated model is better than the comparison model in all cases.

House price index (HPI) and Covid-19 pandemic shocks: evidence from Turkey and Kazakhstan

Purpose This paper aims to investigate asymmetric pricing behaviour and impact of coronavirus (Covid-19) pandemic shocks on house price index (HPI) of Turkey and Kazakhstan. Design/methodology/approach Monthly HPIs and consumer price index (CPI) data ranges from 2010M1 to 2020M5 are used. This study uses a nonlinear autoregressive distributed lag model for empirical analysis. Findings The findings of this study reveal that the Covid-19 pandemic exerted both long-run and short-run asymmetric relationship on HPI of Turkey while in Kazakhstan, the long-run impact of Covid-19 pandemic shock is symmetrical long-run positive effect is similar in both HPI markets. Research limitations/implications The main limitations of this study are the study scope and data set due to data constraint. Several other macroeconomic variables may affect housing prices; however, variables used in this study satisfy the focus of this study in the presence of data constraint. HPI and CPI variables were made available on monthly basis for a considerably longer period which guaranteed the ranges of data set used in this study. Practical implications Despite the limitation, this study provides necessary information for authorities and prospective investors in HPI to make a sound investment decision. Originality/value This is the first study that rigorously and simultaneously examines the pricing behaviour of Turkey and Kazakhstan HPIs in relation to the Covid-19 pandemic shocks at the regional level. HPI of Kazakhstan is recognized in the global real estate transparency index but the study is rare. The study contributes to regional studies on housing price by bridging this gap in the real estate literature.

Organizational foundings, disbandings, and the COVID-19 pandemic

Purpose: The present study aims to understand the effect of the macro-level economic phenomena observed within a specific time interval on the founding (birth) and disbanding (deaths) of organizations in the construction sector of Turkey that has been growing steadily for many years. In addition, the effects of the COVID-19 pandemic were also taken into consideration. Methodology: The construction sector in Turkey was analyzed within the framework of the theoretical infrastructure of organizational ecology, i.e. a theoretical perspective that has not received enough attention, except in North America, as an organizational community, while joint-stock, limited, and cooperative companies were also analyzed as organizational populations. Focusing on the period between January 2017 and December 2020, a number of foundings and disbandings of joint-stock, limited and cooperative companies operating in the construction sector, the house price index and house sales statistics, which are thought to affect these rates, were used as data. Additionally, the COVID-19 pandemic period between March 2020 and December 2020 was included in the analysis as a dummy variable. The ARDL bounds test was used for data analysis. Results: The findings indicate differentiated effects of the house price index, house sales statistics, and the COVID-19 period on both the organizational community of the construction sector and the aforementioned populations. Conclusion: The results, which are expected to contribute to business economics and organizational theories, studies on the construction sector, knowledge of the evaluation of socioeconomic effects of the COVID-19 pandemic and future studies, were obtained in the study.

A STUDY ON THE RELATIONSHIP BETWEEN HOUSE PRICE INDEX AND ITS DETERMINANTS IN MALAYSIA

In Malaysia, House Price is considered high at a certain part of the country causing the lower and middle groups unable to purchase a house. This research examines the long-run relationship and causality effect between House Price Index and determinants of House Price Index. The data was obtained from Valuation and Property Services Department (JPPH), Department of Statistics Malaysia, and Bank Negara. The data was collected over 10 years from 2010 to the first quarter of 2019. Johansen Cointegration Test and Granger Causality Test are applied in determining the long-run relationship and causality effect respectively. The general finding of this study is that the House Price Index shows an upward trend for the past nine years but slightly drop in the first quarter of 2019. This study has found that there is a long-run relationship between the House Price Index and the determinants which are Gross Domestic Product, Interest Rate, Inflation Rate, Population, and Unemployment Rate. Next, all independent variables do not granger cause House Price Index. At the same time, there is only one-way relationship found between House Price Index and Gross Domestic Product, and between House Price Index and Population where House Price Index is identified to granger cause both variables.