Predicting housing prices in China based on modified Holt's exponential smoothing incorporating whale optimization algorithm

The commercial residential industry has high added value and comprehensive economic benefit so the commercial residential industry is naturally a hot-spot issue. The core issue of the commercial housing is the price. This thesis conducts the descriptive statistic analysis of residential real estate prices, urban resident income and other relevant data in China’s 30 provinces (excluding Tibet) from 1998 to 2006. The change trend and the difference feature of both the residential real estate price and the urban resident income in those regions are revealed, which is expected to make a contribution to the macro-control in China’s real estate. In recent years, the real estate market in China is growing rapidly. On the one hand, it plays a vital role in both promoting the national economic growth and improving the living standards of urban residents. On the other hand, some problems in the development of China’s current real estate market have been fully exposed, such as the overheated investment, the unbalance in supply and demand, insufficient financing channels, soaring property prices and so on. In particular, the rapid growth in the housing price has brought challenges to the sound development in both China’s real estate market and the whole national economy and it has also become a hot-spot and difficult issue in the current academia. Such relevant research as whether the rapid growth of China’s housing prices has become disjointed with resident income seriously or not and what the rules of the changes in income and housing prices in China’s different regions are is realistically significant for guiding the micro-control in China’s real estate. I. Index Selection and Disposal of Comparability The samples selected in this thesis are composed of the fluctuating residential real estate prices in China’s 30 provinces (excluding Tibet) from 1998 to 2006, urban resident income and other relevant panel data that are from various years of China Statistical Yearbook. The data of the real estate prices adopts the real estate prices in urban areas. The income indexes adopt the annual per capita disposable income of urban residents. In order to remove the impacts of the price and make indexes of various types had comparability in time series, the disposal of comparability has been conducted in indexes of different types in the thesis and their present value has been turned into the value of the constant price, namely, on the basis of the constant price in 1998, the concrete calculation method is that the housing price is deflated by the housing sales price index and the disposable income is deflated by the consumer price index of urban residents. II. Analysis of Commercial Housing Price Variance among Provinces According to the average housing prices and their growth rates in provinces from 1998 to 2006, thirty provinces, cities and autonomous regions across the country can be divided into three types in accordance with the mean and the growth rate of their average housing prices. It’s found that the provinces, cities and autonomous regions of the three types also have common in geographic areas so they can be divided into such three regions as the eastern region, the central region and the western region on the basis of their geographic areas. For the regional division of the average housing price in China, Figure 1 compares the changes in the average housing prices in the central, western and eastern regions from 1998 to 2006. It’s found that the average housing price in the east is prominently higher than those in the western and International Conference on Education Technology, Management and Humanities Science (ETMHS 2015) © 2015. The authors Published by Atlantis Press 1100 central regions and the trend of its average housing prices is on the rise. In particular, the rising trend of the average housing prices is obvious after 2004. The changes in the average housing prices in eastern and western regions are comparatively similar. However, the average prices in the central region are rising slowly while for the western region, a small decline also appears in its slightly rising process. Besides, the average housing prices in the eastern region surpass those in the western region after 2004. III. Analysis of the Differences in the Income Change among Regions Figure 1: Changes in the housing prices in the central, eastern and western regions Figure 2: Changes in the income in the central, eastern and western regions For the regional division of the average housing price in China, Figure 2 compares the changes in the income in the central, western and eastern regions from 1998 to 2006. It’s found that the per capita income in the east is prominently higher than those in the western and central regions and its trend is uniformly on the rise.

The inflation rate, particularly in South Sulawesi Province from year to year, is found to be very unstable, so that an effort to overcome the instability of the inflation rate is highly needed. One of the efforts that can be used is to carry out a process of forecasting the inflation rate, so that the government can predict the inflation rate properly in order to realize the sustainable economic growth. The aim of this study was to forecast Inflation Cases in South Sulawesi Province. The forecasting carried out in this study used the Exponential Smoothing method. Exponential Smoothing is a method that will take into account average (smoothing) the data of the past exponentially by repeating calculations continuously using the latest data. In this study, 2 Exponential Smoothing methods were compared, namely: Single Exponential Smoothing (SES) and Double Exponential Smoothing (DES) which were used to obtain prediction results and evaluate the results of predictions using the Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE) methods. The smallest MAPE value was obtained when using the Single Exponential Smoothing (SES) method when the value ɑ = 0.1 with the MSE value of 0.5567 and MAPE value of 265.7126 and the Double Exponential Smoothing (DES) method when the value ɑ = 0.3 and with the MSE value of 4,256 and MAPE value of 574,519. Thus, the Single Exponential Smoothing (SES) method was regarded as the best method in predicting the inflation rate in South Sulawesi Province.

This paper examines whether broader money supply (M2) and interest rate as two monetary policy tools may have differently affected housing prices in China. Empirical results show that there is a co-movement between housing prices and M2 in the short run and it becomes more pronounced after 2006 in the medium run. In addition, generally M2 positively affects housing prices. This supports the asset price channel which indicates that an easing monetary policy offers ample liquidity and results in raising the housing prices. The excess liquidity after 2008 spread to housing market, resulting in too much money chasing relatively few assets and triggering a surge in housing prices. On the other hand, we observe that co-movement between housing prices and interest rate is not very evident in most time. Moreover, we find that interest rate has a positive effect on housing prices which is not consistent with the user cost approach and indicates that a contracting monetary policy is not effective in curbing housing market. Not completely liberalized interest rate system and the high return on housing investments reduce the impact of interest rate on housing prices. These findings indicate that money supply is more effective than interest rate as channel to control the housing prices in China. The results are helpful for the scientific formulation of monetary policy for reasonable regulation of the market.

The competition for industrial investment among local governments has distorted the allocation of construction land and brought the rising pressure on residential land prices and housing prices. The existing literature ignores the transmission mechanism between the competition of attracting investment and residential land prices as well as housing prices. Based on the panel data of 104 Chinese major cities from 2009 to 2015, and applying the mediation effect method, this paper investigates the mechanism of attracting investment competition affecting residential land prices and housing prices. The study demonstrates that the competition of attracting investment has significantly increased residential land prices and housing prices. Specifically, the competition of attracting investment mainly raises residential land prices by reducing the supply elasticity of residential land, decreasing the per capita living land area and strengthening the rigidity of housing demand. And the competition promotes housing prices through the paths of increasing government intervention, reducing the supply elasticity of residential land and improving the level of industrial agglomeration. Compared with the housing price, the price of residential land is not sensitive to demand shock because of the influence of the government’s administrative intervention. Furthermore, the competition of attracting investment in the eastern region has remarkably increased residential land prices and housing prices, while the competition in the central and western regions has a significant negative impact on residential land prices and the impact on housing prices is insignificant. And the effect of attracting investment competition in provincial capitals on promoting residential land prices and housing prices is significantly higher than that in general prefecture-level cities. The conclusion of the paper reveals the inefficiency problem under the intervention of local governments. The key to realize the healthy and coordinated development of the real estate industry and the regional balanced development is to reconstruct the incentive mechanism of local governments, improve the market-based allocation of construction land, and optimize the allocation of industrial resources. Compared with the existing literature, the main contributions of this paper lie in three aspects: Firstly, this paper explains the mechanism of attracting investment competition affecting residential land prices and housing prices, deepening the understanding of the political and economic logic behind the Chinese real estate market. Secondly, this paper explores the channels through which the competition of attracting investment affects residential land prices and housing prices. Thirdly, breaking the single research perspective, this paper examines the heterogeneous impact of attracting investment competition on residential land prices and housing prices in cities with different geographical locations and cities at different administrative levels, providing economic logic inspiration for differentiated housing prices in China.

This study aims to empirically study house prices and their relationship with China's critical economic and financial metrics. The key metrics include interest rate, inflation rate, and stock index prices. This study shows that stock index prices do not significantly impact house prices in China. At the same time, long-term interest and inflation rates positively impact house prices in China.Additionally, short-term interest rate possesses a negative im-pact on house prices. This can shed light on policymakers in China when making economic decisions to influence house prices in China. On top of this, this study also compared house prices and real estate stock index prices and concluded that the two differ significantly.

This study addresses the crucial need for accurate forecasting of Foreign Direct Investment (FDIT) trends in Nigeria. FDIT plays a pivotal role in the country's economic growth and development efforts, driving industrialization, infrastructure enhancement, and job creation. However, predicting FDIT accurately is essential for policymakers, investors, and researchers to formulate effective strategies and decisions. This study conducts a comparative analysis of four FDIT forecasting models: Simple Exponential Smoothing (SES), Holt Exponential Smoothing (HES), ARIMA, and NNAR in Nigeria, utilizing R version 4.3.2 and forecast and nnetar packages for implementation. The FDIT dataset is partitioned into 80:20 training-test samples, with models applied to each subset for performance evaluation. Leveraging historical FDIT data, the study evaluates the predictive performance of each model over a specified period, employing error measures such as RMSE, MAE, MASE and MAPE to assess accuracy and reliability. Additionally, residual normality tests and visual inspections validate model assumptions. Results indicate varying levels of accuracy and predictive capability among the models, with HES and NNAR models displaying superior performance compared to SES and ARIMA, evidenced by measure error rates. Residual normality tests affirm the suitability of the models for FDIT forecasting. The study contributes empirical evidence to the existing literature, providing valuable insights into forecasting techniques for FDIT in Nigeria. These insights can guide policymakers, investors, and researchers in selecting appropriate models for forecasting FDIT trends and making informed decisions. Future research avenues could explore advanced modelling techniques and additional variables to further enhance the accuracy and reliability of FDIT forecasts within the Nigerian context.

Power system state estimation is one of the most important functions of power system control centers. In recent years, the complexity of power system state estimation has significantly increased due to the growing number of distributed, including renewable energy sources, electric vehicles, the demand response technologies, and the increased risk of cyber-attacks. Under these conditions, state estimation methods, which consider information about the time-correlation of the power system states have a great potential. The correlation is described by a transition model. The well-known state estimation methods usually use one single model. However, in case of stochastic behavior of the load and generation, it is impossible to assert the adequacy of the chosen model over the entire observation interval. Therefore, the paper proposes a multi-model forecasting power system state estimation method, which has lower errors in comparison with a single-model assessment at the moments of the lowest accuracy of the latter. Multi-model parameter estimation is used based on three procedures of the single-model Kalman filtering estimation and various transition models based on autoregressive and vector autoregressive analyzes, as well as Holt's exponential smoothing. Uniting the single-model estimation has been carried out according to the criterion of the minimum of variance of the resulting estimate. An algorithm of multi-model power system state estimation has been developed. Its version has been analyzed using a three-model forecasting-aided estimation using linear transition models. The state of the IEEE 30-bus test power system has been assessed by the means of simulation modeling. The maximum accuracy increase of the multi-model estimation in comparison with the single model is set up. 26,1 % is for autoregressive analysis; 16,9 % is for vector regression analysis and 37,7 % is for Holt’s exponential smoothing. The proposed method of multi-model state estimation has higher robustness and accuracy at the moments of the lowest accuracy of single-model estimation. It is advisable to use the method to solve control tasks of power systems with rapidly changing dynamic modes.

Predicting the price of a house helps for ascertain the house's selling price in a specific area and assist individuals in determining the ideal moment to purchase a home. Our goal in this machine learning task on house price prediction is to use data to develop a machine learning model capable of predicting housing values in the specified area. We will implement a linear regression algorithm on our dataset. By using real world data entities, we are going to predict the price of the house in that area. For better results we require data pre-processing units to increase the model's efficiency for this project we are using supervised learning, which is a part of machine learning. We have to go through different attributes of the dataset. This project provides us an overview on how to predict house prices using various machine learning models with the help of different python libraries. This suggested model is thought to be the most accurate one for estimating home prices and makes the most accurate predictions. This offers a succinct overview, which is necessary in order to forecast the price of the home. This project consists of what and how the house price model works with the assistance of machine learning technique using scikit-learn and which datasets we will be using in our proposed model. Key Words: house price, lasso regression, ridge regression, R-squared.

Factors related to non-primary housing ownership: Evidence from China General Social Survey

Unlike existing literature that has focused on the relationship between exchange rate and housing price, this paper studies the housing price fluctuations from the perspective of RMB exchange rate expectation to resolve the dilemma “guarantee housing price or exchange rate” after the sub-prime mortgage crisis. This paper shows that housing prices responded negatively to RMB appreciation expectation from 1999 to 2008, and positively from 2009 to 2019. After 2009, exchange rate expectation is the Granger causality of housing prices. After introducing the U.S. Economic Policy Uncertainty (EPU) released by Baker et al.(2016), the explanatory power of exchange rate expectations to housing price fluctuations declines but it's still significant. When EPU increased, housing prices responded negatively after a brief positive response. Besides exchange rate expectation, several unobservable factors with rich economic implications can explain the fluctuations of housing prices in China in the interval of 2006M01–2018M12. The empirical results show that the degree of Chinese government reversal intervention, interest rate spread between China and the U.S., and EPU can explain the exchange rate expectation. The government can control the degree of reversal intervention to affect the exchange rate expectation and realize the housing price control indirectly.

Real estate market analysis has long been an active area of inquiry and one that reveals much about people’s preferences regarding housing attributes. It is well-known that house prices tend to exhibit strong spatial dependency and that they vary across space due to differences in structural and neighborhood characteristics. It is perhaps less well-known but gaining recognition that the influence of various structural and neighborhood characteristics on house prices might vary over space. However, very few, if any, applications in real estate research have recognized and measured the spatial scales over which different factors affect house prices or been able to quantify the ‘intangible’ impacts certain locations have on house prices. Using house price data in King County, WA, this research applies a multiscale extension to GWR, multiscale geographically weighted regression (MGWR), to measure and investigate spatial variations in the processes affecting house prices at varying scales. In a novel attempt, this research quantifies the intrinsic value certain locations have beyond the determinants used to define traditional hedonic price models. The research also demonstrates the utility of MGWR to hedonic price analysis and its ability to identify intricate housing submarkets often overlooked by other techniques.

Through applying spatial statistical analysis, Geographical Weighted Regression (GWR) model and GIS technology, this study aims at finding the relationship between the effects of various factors and spatial distribution of residential house price. The traditional regression models are reviewed firstly, the model without the consideration of spatial characteristics cannot reach very nice precision to simulate the spatial distribution of the house price. In this study, the spatial statistical model, coupled with GIS as well as GWR model, is developed. The proposed model is validated using the house price data in Shenzhen, China, when considering these factors such as the land price, transportation, the distance to the commercial center, the distance to hospital, school, the house type, the brand of the house etc. It is demonstrated that our approach provides an effective model to present the distribution of the residential house price and serve as a tool for house price appraisal during the property tax levy process.

Six types of government policies and housing prices in China

Home-purchase restriction, property tax and housing price in China: A counterfactual analysis

Situated in southern China, Zhaoqing City is a part of Guangdong Province, China. The total administrative area of the city covers 14,891 square kilometers. The data of China’s seventh population census in 2020 showed that the permanent resident population in Zhaoqing City reached up to 4,413,594. Meanwhile, Zhaoqing is one of the cities in the Guangdong‐Hong Kong‐Macao Greater Bay Area. House price analysis and prediction carried out against Zhaoqing City will have directive significance for relevant policies formulated by the local government, residential investment or purchase of consumers, and prediction of house price trend as well as business decisions made by enterprises. By virtue of machine learning and statistical theory, the house price in Zhaoqing City from 2010 to 2020 will be researched, and the house price prediction model of Zhaoqing City will be constructed in this paper with several variables including GDP, proportion of tertiary industry, income of urban residents, fiscal revenue, land price, investment volume in real estate development, permanent resident population, population density, and proportion of urban population in net migration. First of all, the methods of correlation analysis will be utilized, to select variables that are highly correlated with house price data based on correlation coefficients. Then, the model will be constructed for predicting the house price on the basis of multiple linear regression analysis that is conducted with selected variables. Finally, the prediction model will be adjusted gradually based on data with different correlations selected from available data, to realize better imitative effect and more precise predictive effect and select optimum prediction model. By means of the above model, the house prices of Zhaoqing City in 2021 and beyond will be predicted accurately, with preferable fitting effect and prediction effect.