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

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Xiang Wang ◽  
Shen Gao ◽  
Shiyu Zhou ◽  
Yibin Guo ◽  
Yonghui Duan ◽  
...  
Keyword(s):  
Prediction Accuracy ◽  
Price Index ◽  
House Prices ◽  
House Price ◽  
Support Vector ◽  
Data Set ◽  
Support Vector Regression Model ◽  
House Price Index ◽  
Penalty Factor ◽  
Whale Optimization

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

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yeşim Aliefendioğlu ◽  
Harun Tanrivermis ◽  
Monsurat Ayojimi Salami
Keyword(s):  
Real Estate ◽  
Price Index ◽  
Housing Prices ◽  
House Price ◽  
Monthly Basis ◽  
Data Set ◽  
House Price Index ◽  
Content Type ◽  
Asymmetric Pricing ◽  
Long Run

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.

scholarly journals Spatial–temporal neighbourhood-level house price index

2018 ◽  
Vol 11 (2) ◽  
pp. 386-411 ◽  
Author(s):  
Ibrahim Sipan ◽  
Abdul Hamid Mar Iman ◽  
Muhammad Najib Razali
Keyword(s):  
Design Methodology ◽  
Price Index ◽  
House Prices ◽  
House Price ◽  
House Price Index ◽  
Content Type ◽  
Gis Application ◽  
Statistical Application ◽  
Neighbourhood Level ◽  
Spatio Temporal

Purpose The purpose of this study is to develop a spatio-temporal neighbourhood-level house price index (STNL-HPI) incorporating a geographic information system (GIS) functionality that can be used to improve the house price indexation system. Design/methodology/approach By using the Malaysian house price index (MHPI) and application of geographically weighted regression (GWR), GIS-based analysis of STNL-HPI through an application called LHPI Viewer v.1.0.0, the stand-alone GIS-statistical application for STNL-HPI was successfully developed in this study. Findings The overall results have shown that the modelling and GIS application were able to help users understand the visual variation of house prices across a particular neighbourhood. Research limitations/implications This research was only able to acquire data from the federal government over the period 1999 to 2006 because of budget limitations. Data purchase was extremely costly. Because of financial constraints, data with lower levels of accuracy have been obtained from other sources. As a consequence, a major portion of data was mismatched because of the absence of a common parcel identifier, which also affected the comparison of this system to other comparable systems. Originality/value Neighbourhood-level HPI is needed for a better understanding of the local housing market.

Long-term regional house prices cycles. A city-based index for Italy

2017 ◽  
Vol 10 (3) ◽  
pp. 303-330 ◽  
Author(s):  
Laura Gabrielli ◽  
Paloma Taltavull de La Paz ◽  
Armando Ortuño Padilla
Keyword(s):  
Time Series ◽  
Price Index ◽  
House Prices ◽  
Housing Prices ◽  
House Price ◽  
City Centre ◽  
House Price Index ◽  
Content Type ◽  
The Common

Purpose This paper aims to present the dynamics of housing prices in Italian cities based on unpublished data with regional details from the late 1960s, half-yearly base, for all main Italian cities measuring the average prices for three city dimensions: city centre, sub-centres and outskirts or suburbs. It estimates the Italian long-term house price index, city based in real terms, and shows a combination of methods to deal with large time-series data. Design/methodology/approach This paper builds long-term cycles based on the city (real) data by estimating the common components of cointegrated time series and extracting the unobservable signals to build real house price index for sub-regions in Italy. Three different econometric methodologies are used: Johansen cointegration test and VAR models to identify the long-term pattern of prices at the estimated aggregate level; principal components to obtain the common (permanent and transitory) components; and signal extraction in ARIMA time series–model-based approach method to extract the unobserved time signals. Findings Results show three long-term cycle-trends during the period and identify several one-direction causal non-permanent relationships among house prices from different Italian areas. There is no evidence of convergence among regional’s house prices suggesting that the Italian housing prices converge inside the local market with only short diffusion effects at larger regional level. Research limitations/implications Data are measured as the average price in squared meters, and the resulting index is not quality controlled. Practical implications The long-term trends on housing prices serve to implement further research and know deeply the evolution of Italian housing prices. Originality/value This paper contains new and unknown information about the evolution of housing prices in Italian regions and cities.

A New Dynamic House-Price Index

2019 ◽  
Author(s):  
Richard H. Stanton ◽  
Chris Strickland ◽  
Nancy E. Wallace
Keyword(s):  
Price Index ◽  
House Price ◽  
House Price Index

WHAT DRIVES HOUSE PRICE IN MALAYSIA? IN SEARCH OF AN ALTERNATIVE PRICING BENCHMARK FOR ISLAMIC HOME FINANCING

2017 ◽  
Vol 15 (4) ◽  
Author(s):  
Salina Hj Kassim ◽  
Nur Harena Redzuan ◽  
Nor Zalina Harun
Keyword(s):  
Interest Rate ◽  
Price Index ◽  
House Price ◽  
Holistic Approach ◽  
Housing Demand ◽  
House Price Index ◽  
Distributed Lag ◽  
Market Interest Rate ◽  
Auto Regressive ◽  
Conventional Banks

The current practise of the Islamic banks to rely on market interest rate as pricing benchmark for their home financing products has been a subject of intense debate among many parties. Muslim scholars have warned that it is highly discouraged as it could lead to a possible convergence between the practices of the Islamic and conventional banks. This paper intends to address the financing issues in the discussion of human settlement or housing policy by presenting the determinants for house price index as well as looking into the possibility of adopting the House Price Index (HPI) to replace the market interest rate as a pricing benchmark for the Islamic home financing. The study applies Auto-Regressive Distributed Lag (ARDL) method on a model comprising HPI as the dependent variable and a set of independent variables consisting of economic, housing demand and housing supply factors. The findings lead to the formulation of recommendations as a way forward for the Islamic banking industry in particular, and the economy in general. This will require a paradigm shift from basic financing products to a more holistic approach which integrates supply of housing factors, as well as urban planning and urban finance, with human rights and recognizes the need to place and shelter people.

scholarly journals Robust Support Vector Regression Model in the Presence of Outliers and Leverage Points

2017 ◽  
Vol 11 (8) ◽  
pp. 92
Author(s):  
Waleed Dhhan ◽  
Habshah Midi ◽  
Thaera Alameer
Keyword(s):  
Regression Model ◽  
Support Vector Regression ◽  
Support Vector ◽  
Data Set ◽  
Support Vector Regression Model ◽  
Leverage Points ◽  
Linear Relationships ◽  
Linear And Nonlinear ◽  
Abnormal Points ◽  
Robust Support Vector Regression

Support vector regression is used to evaluate the linear and non-linear relationships among variables. Although it is non-parametric technique, it is still affected by outliers, because the possibility to select them as support vectors. In this article, we proposed a robust support vector regression for linear and nonlinear target functions. In order to carry out this goal, the support vector regression model with fixed parameters is used to detect and minimize the effects of abnormal points in the data set. The efficiency of the proposed method is investigated by using real and simulation examples.

A house price index based on the SPAR method

2009 ◽  
Vol 18 (3) ◽  
pp. 214-223 ◽  
Author(s):  
Paul de Vries ◽  
Jan de Haan ◽  
Erna van der Wal ◽  
Gust Mariën
Keyword(s):  
Price Index ◽  
House Price ◽  
House Price Index
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