House Price Changes Research Articles

Can Social Media Information Amplify Short-term Housing Price Changes? An Investigation in China’s Major Cities

Can Social Media Information Amplify Short-term Housing Price Changes? An Investigation in China’s Major Cities

Research on the Factors Affecting House Price using Multiple Linear Regression Model

This article aims to identify the factors that affect the change in house prices. In this paper, the Multiple Linear Regression Model is used to analyze the factors with 1000 random samples from the USA which was collected from the 2nd of May in 2014 to the 10th of July in 2014. Based on the datasets, 13 variables (bedrooms, bathrooms, sqft_living, sqft_lot, floors, waterfront, view, condition, sqft-above, sqft_basement, yr_built, yr_renovated, street, city) are selected as independent variables, and used in scatter diagram to show the correlation with the dependent variable price. Turns out that 3 variables have not shown a significant correlation with the price (yr_built, yr_renovate, city). To find which of the factors have the most significant impact on the change in house prices, VIF value is used to compare the collinearity of those 10 variables. Finally turns out that only 5 variables (bedrooms, view, condition, waterfront, sqft_lot) show the most significant effect on the price of housing. Overall, the main factors affecting the price of housing in the USA were the number of bedrooms and views next to the house, as well as the conditions needed for buying the house.

House Prices and Rents

Abstract Variation in monthly metro area house prices unrelated to expected rents clouds the information about future rents in price-rent ratios and lagged changes in house prices. The variation in house prices unrelated to expected rents is, however, correlated across areas, and the problem is mitigated by measuring rent growth regression variables net of their monthly cross-section (across-area) means. This control for price variation unrelated to expected rents substantially enhances the information about future rents that we extract from price-rent ratios and lagged changes in house prices. (JEL C51, C58, D53, G14)

Research on the Prediction of the Housing Pricing in China

As an important cornerstone of industrial development, real estate has been widely concerned by people. A house can not only provide a place of peace for people, but also provide a certain source of income for many people. A stable house price will give people confidence in their economy. After 2007, there have been obvious changes in house prices, which further increase the difficulty of forecasting and reduce the accuracy of forecasting. As a typical forecasting model, linear regression is favored by scholars all over the world for its accuracy and adaptability. This paper will use this model to forecast the house price in the next year. The conclusion is as follows: In the coming year, the overall trend will show a more obvious upward trend. Therefore, it is recommended to invest in real estate in the short term. At the same time, this paper believes that ensuring the stability of domestic housing prices can improve people’s quality of life and enhance the status of the country in the hearts of the people.

Analysis of Influential Factors of House Prices in Shenzhen

The change of house prices in Shenzhen can reflect the trend of house price changes in China, to a certain extent. This text chose the Factor Analysis Method to Specific analyze influential factors. This text put F1 as a variable, which included Total Floor Space of Residential Buildings Sold, Permanent Population, Total Floor Space under Construction of Commodity Housing, Investment in Real Estate Development, Gross Domestic Product, Local Budgetary Revenue and Expenditure. Through analyzing the data, this text deduced that Longgang, Baoan and Nanshan districts had above average values on F1. This text put F2 as a variable, which included Per Capital Disposable Income of Permanent Households and Growth Rate of Real Estate Investment, etc. Through analyzing the data, this text deduced that Nanshan, Luohu, Futian districts had above average values on F2. This text can conclude that Nanshan, Baoan and Futian Districts had a great influence on house prices in Shenzhen.

Residents’ willingness to pay to avoid crime

How much is a resident willing to pay to avoid a crime in the neighborhood? House price changes following a crime do not fully reflect the willingness to pay to avoid crime. Besides prices the market’s liquidity needs to be taken into account in this context. We detect market freezes following shootings, assaults, robberies, and burglaries. We propose a model, supported by empirical evidence, that combines the price and quantity dimensions into a single measure: economic welfare. The willingness to pay depends on the type of crime and averages between 7% and 18% of house valuations. These predictions are manifolds of the effect on prices estimated in this paper (0%–10%) and documented in the literature. Overall, our findings highlight the importance of considering market liquidity when examining the impact of crime on the real estate market.

An empirical research on the influence of house price on the consumer market based on monthly statistics in Shanghai

Consumption plays a crucial role in peoples daily lives. In recent years, Chinas consumer market has faced urgent needs for consumption transformation and heavy pressure from economic downturn. As an important component of everyday consumption and wealth savings, there is a close relationship between changes in house prices and the consumer market. There has been a heated debate in the academic community regarding the positive or negative relationship between the two. The study keeps focus on the changes of consumer market, attempting to reveal the effects of house price has. This article extracts monthly consumption and housing price data in Shanghai from 2014 to 2024, strictly arranges them in chronological order, and uses the ECM model to handle the instability of time series data. The conclusion of this study is that the rise in housing prices can stimulate consumption. Also house prices have a controlling effect on the fluctuations of the consumer market.

The Impact of Urban Planning on House Prices in China

Urban planning is a major part of urban development and leads the future direction of the city. In different times and social situations, urban planning can change, and this change can have an impact on house prices. This paper analyzes the changes in house prices due to urban planning, with China as the main subject. China's real estate market has changed significantly. Housing prices have changed more dramatically and are more susceptible to external factors. Through the collection of data on house prices in Beijing, Jiangsu, and other regions and the development of urban planning, it summarizes the important factors of urban planning affecting house prices and provides an in-depth analysis of different factors. Through the conclusions obtained from different analyses, this essay summarizes the relationship between urban planning and house prices and provides certain suggestions to the government on plans and improvements to address the issues involved.

Investigating the impact of housing price increases on consumption: heterogeneity by age, tenure and housing quality

PurposeThe purpose of this paper is to understand the distributional impact of house price increases on consumption in the context of the energy transition.Design/methodology/approachThis study draws from two micro cross-sectional datasets, the English Housing Survey (EHS) and the Living Costs and Food Survey (LCFS) to study the Marginal Propensity to Consume (MPC) out of changes in house prices. By employing pseudo-panel regressions, the paper examines the impact of house price changes on consumption among diverse household types.FindingsThis paper finds varying consumption responses to house price changes across age and tenure groups. Older homeowners tend to increase consumption when house prices rise. In contrast, middle-aged individuals, often renters or mortgage holders, reduce consumption in response to price increases. The youngest age group also experiences increased consumption but to a lesser degree than the oldest group. Energy-efficient homes are related to lower consumption across all tenure levels. However, when interacted with house prices and age, the estimates are positive, pointing to an unequal accrual of property premiums depending on housing market positions.Research limitations/implicationsThe main limitations stem from data constraints. First, using a pseudo-panel approach hinders control for unobservable selection bias. Additionally, while robust under cross-validation and specifications tests, the energy efficiency variable imputation results in a low number of energy-efficient homes. Due to heterogeneous responses to rising house prices, this paper contends that an energy transition model that subsidises homeowners’ renovation is likely to produce a negative impact on consumption among younger and middle-aged households.Originality/valueThis paper contributes to the MPC literature by incorporating energy efficiency as a key variable. It draws from recent data to obtain new estimates. By highlighting shifts in consumption patterns the paper contributes to a well-established body of literature with renewed policy relevance regarding housing retrofit.

Productivity Shocks of Dominant Companies and Local Housing Markets

We extend the literature on the influence of firm-level characteristics on housing markets by exploring the association between the labor productivity shocks of dominant firms and local housing prices. Using a sample of all U.S. firms from COMPUSTAT during 1980-2017, we find that the aggregate shocks of labor productivity of dominant firms at the metropolitan statistical area (MSA)-level explain for a significant portion of the local housing price changes in MSAs while controlling for other housing price determinants. About a year or more is required for the shocks to propagate through the local housing markets, which make them a viable predictor of future housing price. The productivity shock – housing price relation is stronger in areas that have more concentrated high-tech dominant firms or where dominant firms have closer links to their local non-dominant industry peers. Shocks are also more influential during economic expansion than economic contraction. Furthermore, the relation also exists at the zipcode level but the shocks propagate faster than at the MSA-level. The findings provide helpful insights for real estate practitioners and policymakers, especially in areas with a higher concentration of large companies.

Do housing prices decrease when interest rates go up? Housing prices and interest rates: understanding the positive correlation across five countries

This paper investigates the relationship between changes in interest rate and house prices from the period 1975 to 2023 in five countries: United States, United Kingdom, China, Germany and Indonesia. Results show that contrary to the paradigm of real estate pricing, housing prices increase with interest rates. Furthermore we find that changes in house prices are independent of monetary policies and interest rate changes set by the central bank. Economically developed nations exhibit a stronger correlation between the two variables. We demonstrate that this is due to the 2008 financial crisis, where houses fell by nearly 30%, creating a fear of recession and the resulting policy of low interest rates to encourage investment.

A Regional Analysis of the Impacts of Changes in Housing Values on Property Tax Assessment During and After the 2008 Financial Crisis

This study examines the relationship between changing house prices and assessed value in the Detroit Michigan region. Using parcel-level data from 2008 through 2016, we construct a Hedonic Housing Price Index and an Assessed Value Index for 120 Michigan assessing jurisdictions in the five-county region surrounding Detroit (Macomb, Monroe, Oakland, Washtenaw, and Wayne). Our findings are consistent with the literature, indicating at least a three-year lag between changes in house prices and changes in property assessments. Communities considered to be distressed tended to over-assess properties before the crisis, but these communities recalibrated assessments to better align with market conditions after the financial crisis. Our evaluation also shows that while assessed values decreased more slowly than housing prices fell during the crisis, assessed values tended to fall to even deeper levels than housing prices over a longer time horizon. JEL Codes: H71, H83, G01

Positive or Negative: The Heterogeneities in the Effects of Urban Regeneration on Surrounding Economic Vitality—From the Perspective of Housing Price

Urban regeneration is a sound, sustainable urban development strategy globally. In China, promoting urban regeneration has become the national sustainable urban strategy. Under the resource constraint, it is necessary to understand what benefits different types of urban regeneration projects can contribute to urban development. Much research has contributed to evaluating the benefits of urban regeneration on the project scale. The systematic investigation of their effects on surrounding economic vitality, especially the comparison between different types of projects, is relatively downplayed. This paper aims to evaluate and compare the effects of urban regeneration projects on surrounding economic vitality by calculating the change in housing prices. Chongqing is selected as the case city. Based on the housing transaction data from 2015 to 2021, a staggered difference-in-difference method is employed to capture the results. The finding shows that the overall effects are significantly negative, especially within a 400–800 m radius. Higher investment or better project location relates to stronger negative effects. Moreover, the differences in effects among the three types of urban regeneration projects are clearly revealed from diverse perspectives. It provides a valuable reference for policymakers and urban planners to make urban regeneration planning better by considering comprehensive benefits.

Unveiling investors' substitution behavior: Stock trading decisions in response to housing market dynamics

Utilizing positive quasi-random shocks to local housing prices in Shanghai, we show that stock investors who experienced significant returns from the real estate market traded less actively, took less risk, and spent less effort trading. We confirm the effect of housing price changes on investors' trading behavior in a national sample. Our findings suggest a substitution effect between the real estate and stock market and highlight the importance of understanding investors' trading behavior in light of intertemporal variations in other asset market.

Research on the Relationship between Influencing Factors and House Prices Changes

In recent years, the change in house prices has also become a main factor affecting economic changes. There are lot of research shows that the relationship between economics and house prices. This study focuses on how house prices change and what factors can lead to the change in house prices. The data from the Kaggle website, containing property prices in Delhi. By using the linear regression model, calculate 12 factors which are Price, Area, Bedrooms, Bathrooms, stories, main road, guestroom, basement, hotwaterheating, airconditioning, parking, prefarea, and furnishing status to study the degree and trend of impact on house prices changes. The results show that area, bathrooms, stories, guestroom, basement, hotwaterheating, airconditioning, parking, prefarea will have a significant positive impact on price, and furnishing status will have a significant negative impact on price. Bedrooms have no impact on price. This study opens a new perspective and giving society a new thinking angle for the study of housing prices changes.

Spatial and temporal diffusion of property prices and sales across British regions

Purpose The purpose of this study is to reveal the dynamics of house prices and sales in spatial and temporal dimensions across British regions. Design/methodology/approach This paper incorporates two empirical approaches to describe the behaviour of property prices across British regions. The models are applied to two different data sets. The first empirical approach is to apply the price diffusion model proposed by Holly et al. (2011) to the UK house price index data set. The second empirical approach is to apply a bivariate global vector autoregression model without a time trend to house prices and transaction volumes retrieved from the nationwide building society. Findings Identifying shocks to London house prices in the GVAR model, based on the generalized impulse response functions framework, I find some heterogeneity in responses to house price changes; for example, South East England responds stronger than the remaining provincial regions. The main pattern detected in responses and characteristic for each region is the fairly rapid fading of the shock. The spatial-temporal diffusion model demonstrates the presence of a ripple effect: a shock emanating from London is dispersed contemporaneously and spatially to other regions, affecting prices in nondominant regions with a delay. Originality/value The main contribution of this work is the betterment in understanding how house price changes move across regions and time within a UK context.

Is There Enough Housing Production? It Matters Which Indicators Are Used to Answer

McClure and Schwartz argue that there is no US housing shortage for the 2000-2020 period because: (1) we added more dwellings than households; (2) vacancy rates are not low by historical standards; and (3) current headship rates are unrelated to the ratio of recent growth in dwellings to growth in households. I argue that these indicators offer misleading guides for addressing policy-relevant questions related to the supply of housing because household formation is endogenous to changes in the housing stock. I demonstrate the shortcomings of these indicators with a stylized graphic/numerical analysis of short-term and long-term housing market equilibrium and adjustment dynamics in two hypothetical metropolitan areas. Changes in household size (adjusted for demographic and income distributional alterations) and changes in housing rents and prices are more appropriate indicators for making housing policy choices, implying that current concerns over the responsiveness (long-run elasticity) of housing supply are indeed justified.

Dynamism Diminished: The Role of Housing Markets and Credit Conditions

We estimate the effects of house price changes on young-firm employment shares and industry-level employment growth in local economies. A novel test shows that house price effects on local economies work through wealth, liquidity, and collateral effects on the propensity to start new firms and expand young ones. Aggregating local effects to the national level, our estimates imply that housing market ups and downs play a major role—as transmission channel and driving force—in medium-run fluctuations in young-firm employment shares in recent decades. We also find a distinct and smaller role for locally exogenous loan-supply shifts. (JEL D22, E24, E32, G21, L25, R11, R31)

The Role of Speculative Demand in Housing Price Changes in Turkey

Housing accounts for the largest portion of households' total assets in Turkey. It is an economic sector that requires careful analysis, especially considering the government's housing sector support policies and the significant price increases in recent years. This study utilizes the theoretical model developed within the Efficient Markets Hypothesis framework and the SVAR methodology to estimate house price changes. These changes are then decomposed into structural shocks, including supply, residential demand, and speculative demand shocks. The empirical results highlight the significant role of speculative demand in driving house price changes, with house price expectations being the main factor influencing speculative demand shocks. The behavior of the banking sector and interest rates also play a significant role in this process. Consequently, policies to stabilize house prices should consider these factors and not solely focus on credit regulations when consumer expectations are the driving force behind house price changes.

한국 주택가격의 동태적 변화 연구

Housing is one of the essential goods that determine the conditions of life. However, housing prices fluctuate widely depending on macroeconomic conditions, making it an important research topic. This study analyzes the dynamic changes in quarterly housing prices from 2006 to 2023, using GARCH model, Granger causality test, and vector error correction model(VECM). The main finding are as follows. First, there are inertial feedback effects in house price changes. Second, there is clustering and long-run persistence in the dynamics of house price volatility. Third, we find significant effects of housing loans on the demand side and housing construction permits and unsold housing units on the supply side on house prices. Housing loans have a positive effect in the short run, while housing supply variables such as housing construction permits and the number of unsold housing units have a negative effect in the long run. These results of our analysis suggest that the housing market follows the market principle that the role of housing supply and demand works well, and that housing prices have inertial characteristics with spikes and crashes depending on the economic environment. Housing policy-makers need to take these findings into account when formulating housing policies.