Real Estate Supply Research Articles

Time lag analysis of real estate supply policies for housing construction projects

ABSTRACT Real estate is not only a vital component of the economy, but it also has a considerable influence on household finance and various social and cultural phenomena. South Korea is experiencing an accelerating trend of economic polarization, which is being driven by low interest rates and growth paradigm. It’s important to recognize that asset disparity, rather than income differences, is the primary cause of this polarization. Therefore, it is crucial for government entities to intervene through real estate policies to address this issue. However, government policies have at times failed to meet expectations, showing limitations in stability and reliability. They have failed to provide precise responses to demand and supply, ranging from implementing excessive regulation policies to considering occupancy rates as current supply rather than as results of previous policies. To address these issues, we applied cross-correlation analysis to derive time lags between procedures in housing construction projects at the national level in South Korea. This approach can provide essential insights to enhance the effectiveness of supply policies by identifying the temporal discrepancies that occur until actual supply is realized.

CALCULATING THE PRICE OF COMMERCIAL REAL ESTATE IN THE CZECH REPUBLIC

Property prices have surged recently, mainly due to limited real estate supply amid high demand supported by a large monetary base. This has driven up rental prices, especially in major cities compared to their peripheries. This manuscript examines rental prices in Prague, Brno, Ostrava, Pilsen, and České Budějovice from March to October 2023. It uses distribution functions, correlation coefficients, contour heat maps, and parameterized regression to define a probability price interval for office properties, identify a positive correlation between area and rental prices, and develop an algorithm for setting appropriate rental prices. The findings are useful for practitioners, real estate agents, and government institutions, though they are closely linked to macroeconomic variables. Future potential options are discussed.

중국 부동산 공급 각도에서 본 유령도시화 현상의 원인 연구

중국 부동산 공급 각도에서 본 유령도시화 현상의 원인 연구

THE IMPACT OF THE DIGITAL ECONOMY ON URBAN HOUSE PRICES: COMPREHENSIVE EXPLORATIONS

Internet technology and data-driven innovations are changing the way people live and work, which could have an impact on real estate supply and demand. Therefore, this study focuses on the relationship between urban digital economy growth and urban house prices. First, the empirical model shows that the digital economy has an inverted U-shaped impact on urban housing prices. The mechanisms underlying this relationship were then examined. The results indicated that fixed asset investment, government expenditure, and the urban environment influence the relationship between digital economy growth and urban house prices. Finally, a heterogeneity analysis revealed that the digital economy does not affect house prices in Tier 1, New Tier 1, and Tier 2 Chinese cities, but increases house prices in Tier 3 cities. In Tier 4 and Tier 5 cities, this relationship was found to be inverted U-shaped. These findings offer valuable insights to policymakers in China in balancing the growth of the digital economy and the stability of house prices.

The Externality Impact of Urban Rail Transit on Urban Real Estate Prices in China

China has the largest and fastest growing urban rail transit system in the world. The system plays an important role in China's urbanization process, city governance and residents' daily commutes. The development of urban rail transit (URT) has influenced China's urban spatial structure and real estate supply, and has produced externalities on real estate value, including both positive and negative externalities. The research shows a positive correlation between URT and China’s real estate prices in general. The closer rail transit systems are, the greater the premium on real estate prices. In addition, the impact of rail transit on real estate prices shows a trend of first increasing and then decreasing. Different land use types and urban areas will also be affected. The research also found that the three stages of URT construction: planning, engineering construction and traffic operation have different effects on housing prices. At the same time, the impact of URT on land price will also change with China's economic situation and urbanization process. These influencing mechanisms above between URT and real estate prices may be linked to some factors. This research attempts to explain it by introducing urban spatial and land rent transformation, changes in real estate supply and demand, gentrification process, and different employment and housing pattern. This research points out the shortcomings and possible research directions of existing studies. In order to provide a reference for academic research and government policy making.

Research on the Financial Credit Risk Management Model of Real Estate Supply Chain Based on GA-SVM Algorithm: A Comprehensive Evaluation of AI Model and Traditional Model

By combining machine learning AI model with traditional credit risk measurement model, this paper aims to more accurately measure the credit default risk of real estate enterprises. Through professional credit management knowledge, the integration of these two models can fill the gaps of traditional models outside of financial data, and use the research experience of traditional models in the financial field to provide guidance for machine learning. This paper focuses on the key issues of capital intensity, exploration of Supply Chain Finance Growth and Credit Risk Control within the Real Estate Sector. By combining qualitative and quantitative methods. Through literature research and empirical analysis, this paper develops a supply chain finance credit risk assessment index system tailored for small and medium-sized enterprises operating in China's real estate sector. The index system comprises 23 risk assessment indicators, and comprehensively considers many factors such as corporate financial status and market environment. Then, a credit risk assessment model for small and medium-sized real estate enterprises is developed from a supply chain finance perspective, utilizing genetic algorithm optimization for Support Vector Machine (GA-SVM)., and the advantages of this model compared with the traditional model in accuracy and generalization ability are verified through comparative analysis of experiments. The research results can help reduce the information asymmetry in the real estate industry, enhance the credit rating evaluation process for small and medium-sized enterprises, and furnish references to aid commercial banks in credit and credit risk management within the real estate supply chain finance sector.which has important theoretical and practical value.

房地产供需及房价的数学模型探究

房地产供需及房价的数学模型探究

Digital Nomads' Willingness to Stay – a Combined Analysis of SEM and fsQCA Results

Lisbon is a multicultural city with a business center that hosts major technology companies and professionals in the field, including digital nomads. High demand for tourist accommodation and the arrival of foreigners to work in the country have led to an imbalance between the supply and affordable cost of real estate for residents of the capital. This work aims to survey and determine whether digital nomads in Lisbon intend to settle in the city or are just passing through. To identify the combination of factors that influence digital nomads’ decision-making, we conducted a quantitative study using survey data tested through structural equation modelling and configurational analysis by fsQCA. The results show that while the decision to stay in the city is heavily influenced by community attachment, we found three configurations that reveal distinct patterns of digital nomads’ intention to stay in the city. These results suggest that policymakers need to consider that digital nomads are not a homogeneous group. This study makes an important contribution to the understanding of behavioral intentions and is, to the authors’ knowledge, the first to identify different configurations based on digital nomads’ intentions to stay in a city.

Analysis On the Influencing Factors of Housing Price in China

In order to recognize and analyze the influencing factors of housing price, based on the previous literature, this paper mainly examined 3 major factors: Supply Factors, Demand factors, and Government Policy Intervention in Chinese Housing system. It was indicated that, for supply factors, development investment in the real estate market has a considerable impact on the supply and price of real estate. While for the Demand respect, we further divided it into two factors including population and level of urbanization. The results showed that although total population has no clear relation with the housing price, population aging was a positive relation with the real estate. Also, the level of urbanization only has positive relations with some parts of China. Government policy intervention is unique in China, therefore, the results of this paper could provide some useful information for the investors and governors in analyzing Monetary policy, Housing credit system, Land system, and Taxation system. Generally speaking, our paper generates a better reasoning of Chinese Housing Price.

Research on Relationship Between Population Structure and Real Estate Investment with Big Data

With the rapid development of "Internet +", the current supply and demand of real estate have changed. The introduction of big data technology to analyze the demographic structure and real estate market-related information improves the scientific nature of real estate investment decisions. Scientifically defining the guiding role of population structure on real estate development investment is helpful to promote the coordinated development of real estate industry. Based on this, we build a theoretical model of the real estate development investment data analysis support system by selecting the panel data of each province from 2002 to 2018 and using the econometric model. The impact of population structure is tested on real estate investment from the national level and sub-regional level. The specific effect of population structure is also analyzed on investment under the urbanization rate. The research result shows that the total dependency ratio of the population and the youth dependency ratio is negatively correlated with real estate development investment at the national level. The elderly dependency ratio is positively correlated with real estate development investment. At the regional level, the elderly dependency ratio is only in the central region. As a positive impact, the juvenile dependency ratio affects real estate development investment, but the degree of influence is different. From the perspective of the urbanization process, the higher the urbanization rate, the less binding the juvenile dependency ratio on real estate development investment. In view of this, from the perspective of population structure, suggestions are put forward on the supply-side structural reform of the real estate industry.

The Influence of the COVID-19 on Hong Kong Housing Price

This article mainly discusses the impact of the epidemic on housing prices in Hong Kong. Today, COVID-19 is still a pandemic with a high impact on a global scale, affecting people's lives in many ways. Compared with before the epidemic, Hong Kong housing prices have a certain downward trend. The purpose of this study is to explore the proportion of the epidemic and the factors that are mainly affected by the epidemic. Through the correlation analysis and regression analysis of the house price index, the price index and the impact of real estate investment, it is found that the negative impact of the current epidemic on housing prices is relatively obvious, and its impact is gradually analyzed. Finally, it is concluded that the epidemic affects the supply and demand of real estate by affecting the number of people, and ultimately affects housing prices. However, because the government and real estate developers have introduced corresponding policies to deal with the epidemic, it is difficult for the future house price expectations to be significantly reduced.

On the challenges of inter-organisational data in real estate: the role of knowledge sharing

PurposeTo take full advantage of new technologies (e.g. machine learning), a consistent and more comprehensive knowledge sharing across organisational boundaries is required. The real estate industry often faces this challenge as well. The paper aims to approach this circumstance by analysing the real estate supply chain (RESC) and aims to identify ways to achieve a more disruption-free flow of information.Design/methodology/approachTo answer the research question, a cross-sectional study was conducted using a standardised online questionnaire and structural equation modelling. The majority of the study participants came from German-speaking countries. The structural equation model was used to examine the relationships between the factors inter-organisational relationship, knowledge sharing skills and knowledge sharing.FindingsThe authors found that inter-organisational relationship has a significant influence on knowledge sharing and thus on the occurrence of information flow disruptions. Aligning shared goals of parties plays a major role to improve inter-organisational relationship. Control and supervision does not show any significant effect. Knowledge sharing skills in turn have an influence on knowledge sharing.Practical implicationsThe alignment of interests and the creation of trust should be established in the future as core elements within the framework of contract negotiations, the lived contract and the contract itself, in order to reduce disruptions in the flow of information.Originality/valueFrom the perspective of knowledge sharing, this study attempts to identify necessities within the framework of contractual design options that simplify and promote the inter-organisational use of algorithms to increase transactional efficiency.

The identification of spatial bubbles in a real estate market

There is great interest in identifying and modelling real estate market bubbles over time. However, the spatial dimension of those bubbles gets less attention. While building on the theory of real estate market equilibrium and formation of market bubbles, this research focuses on the spatial bubbles of the market value of apartments inside a city. This approach adds another dimension to the phenomena of real estate market bubbles. The scientific problem of this study is whether the spatial differentiation of real estate encourages the formation of spatial bubbles in real estate market. The object of research is the spatial bubbles in real estate market. The aim of research is to identify the impact of spatial bubbles on real estate market value. Because of the nature of real estate market, the supply and demand of real estate does not match, therefore real estate almost never reaches the equilibrium market value and bubbles appear. The main idea behind this research is that the supply and demand equilibrium model can be applied not only to the changes over time but also across space inside a city. The spatial bubbles appear because of unexplained variance. The empirical research focuses on unexplained variance across space of market value of homogeneous apartments in two major cities in Lithuania. The prominent variance is eliminated through careful selection of projects and control variables such as floor and size of an apartment, distance to civic points and location. Up to 39% of unexplained variance of apartment market value across space was found when looking at 3D diagrams of residualised apartment market value of the selected cities. The main reasons behind formation of those spatial bubbles should be addressed by further research for deeper understanding of the phenomenon of spatial bubble formation.

Real Estate and Construction Sector Dynamics Over the Business Cycle

This paper explores the property prices and investment dynamics over the business cycle when there is competition between households and firms for real estate. We introduce a construction sector into an RBC framework, which uses land, capital and labour to produce both commercial and residential real estate. This market structure activates a real estate substitution channel, where an increase in demand for residential real estate also increases the cost of producing commercial structures, which crowds out commercial real estate investment. In general, we find that the residential/commercial land allocation acts as an anchor for the allocation of its real estate investment counterpart; however, there are notable separations, particularly following the financial crisis where there was a simultaneous fall in residential and commercial investment. Our results indicate that whilst residential real estate prices were predominately driven by increases in its demand in the buildup to the financial crisis, the fall in demand for commercial real estate played a significant role in generating price falls for both types of real estate in the aftermath. Furthermore, falls in the overall supply of real estate played an important role in reducing real estate investment which put upward pressure on prices throughout the past two decades.

Mortgage volume and macroeconomic factors: Evidence from Turkey

This paper investigates the impacts of several macroeconomic variables on Turkey's volume of mortgage loans. Johansen cointegration test, vector error correction model, Granger causality tests, variance decomposition, and impulse-response analysis is employed for the econometric analysis to show short and long-run relationships between the variables using time series monthly data from January 2010 to March 2020. Paper results demonstrate that growth of housing credit size negatively correlates with mortgage interest rates, US Dollar/Turkish Lira exchange rate and level of real estate supply. At the same time, there is a positive correlation with house prices. Causal relationships between mortgage volume and macroeconomic indicators are bidirectional for all variables, except for mortgage interest rates. There is a one-way causality relationship from mortgage rates to mortgage loan volume. Econometric analyses show that the recent steep depreciation in the Turkish Lira hurts the Turkish mortgage market. In conclusion, a stable economic environment is essential to build a robust mortgage market.

Anomalies of the Housing Market in Albania

The real estate sector is one of the most important sectors of the economy for developing countries. The housing market is directly related to the performance of economy. The impact of this market is measured through the volume of various transactions, such as real estate sales, lease contracts, construction contracts, import transactions, foreign exchange, transactions of financial intermediaries and real estate agencies, employment contracts, etc. The real estate market is directly influenced by housing policies, the level of financial system development, visible additional costs (tax rates and credit costs) and its invisible costs (which is informality and information asymmetry). Hypotheses: The housing market operates according the rules of the supply-demand and the factors that affect the fluctuation of housing prices. The purpose of the article is to evaluate the demand factors in the performance of house prices in Albania. Methodology: The study employs an exploratory analysis based on the literature review, the secondary data and empirical analysis. The authors make a comparison between the factors identified by the literature review, the analysis of the secondary data for the Albanian real estate market, and statistical relationships of individual factors as well: GDP/capita, Exchange rate, Interest rates in ALL, Interest rates EURO, Remittances and the level of mortgage loans in EURO. In the conclusions of the paper, some of the factors that have directly influenced the fluctuations in real estate prices in Albania are: the demand and the supply of real estate; the change in the value of the functional currency; state intervention through fiscal policies and urban planning; credit financing to businesses and individuals, etc.

Real Estate and Construction Sector Dynamics Over the Business Cycle

This paper explores the property prices and investment dynamics over the business cycle when there is competition between households and firms for real estate. We introduce a construction sector into an RBC framework, which uses land, capital and labour to produce both commercial and residential real estate. This market structure activates a real estate substitution channel, where an increase in demand for residential real estate also increases the cost of producing commercial structures, which crowds out commercial real estate investment. In general, we find that the residential/commercial land allocation acts as an anchor for the allocation of its real estate investment counterpart; however, there are notable separations, particularly following the financial crisis where there was a simultaneous fall in residential and commercial investment. Our results indicate that whilst residential real estate prices were predominately driven by increases in its demand in the buildup to the financial crisis, the fall in demand for commercial real estate played a significant role in generating price falls for both types of real estate in the aftermath. Furthermore, falls in the overall supply of real estate played an important role in reducing real estate investment which put upward pressure on prices throughout the past two decades.

Forecasting the volume of mortgage loans with open Internet data in the period of noticeable changes in the Russian mortgage market

Forecasting the volume of mortgage loans is important due to the necessity to correctly plan money flows and to ensure the liquidity of assets of credit organizations. At the time of the study, there are noticeable changes in the mortgage market in Russia associated with the coronavirus pandemics, the introduction of the preferential mortgage program, etc. In such conditions, flexible methods to make the forecast are specially required. Motivated by this, we develop an open Internet data-based approach for forecasting the volume of mortgage loans in the residential real estate market of Saint Petersburg (Russia). In particular, we first critically select sources of open Internet data about the volume of mortgage loans, real estate supply and demand, as well as data on relevant economic indicators. Furthermore, we analyze the selected data and use the most suitable ones as predictor variables in several statistical and artificial neural network-based models for forecasting the volume of mortgage loans (the target variable). It turns out that the usage of Yandex open data on the search query “buy an apartment in Saint Petersburg” (in Russian), the Russian Central Bank key rate and the dollar/ruble exchange rate data as predictor variable improves the forecasting quality. In particular, the ARIMAX model with the above-mentioned predictor variables outperforms all the models under consideration (Baseline, ARIMA, LSTM, MultiVariate LSTM, Transfer Learning) in terms of Mean Absolute Percentage Error (MAPE).

Risk Analysis of the Real Estate Financial Market Based on Risk Energy Theory

Beginning with real estate financial risk and risk energy theory, combined with empirical research, this article analyzes real estate financial markets in different stages and calculates the profit and loss (the ratio of the release of risk energy to the risk energy) in order to judge whether the risk energy has exceeded the system tolerance and to play a warning role. First, this article analyzes uncertain factors in the real estate market, builds a real estate supply and demand network, determines system risk characteristics, corresponding profit and loss categories, quantitative and qualitative factors, and the risk of energy release and the collapse of boundary conditions. The real estate market is approached using both univariate and multivariate analyses, dividing the real estate market into three stages and probing mergers and transformation conditions for different levels of risk. Finally, the authors analyze whether the financial risk energy released by real estate market exceeds the capacity of the system by calculating probability, risk, which play a warning role.

A Study on System Improvement to Activate the Supply of Public Rental Housing by Private Sector in the Station Area of Daejeon Metropolitan

Looking at the comprehensive housing plan recently announced by the Ministry of Land, Infrastructure and Transport, various residential welfare policies and supports are planned for homeless low-income households. At a time when controversy over the rise in prices and supply of housing and real estate emerged across the country, Daejeon Dream Town was introduced to promote housing stability and local economy for young people and newlyweds. Daejeon Dream Town is expected to reduce the budget in Daejeon while relaxing the system to induce private development in the accessible station area and the nodules of public transportation, and providing apartment houses in a location suitable for the needs of youth and newlyweds with part of the increased development profit. In order for Daejeon Dream Town to become more revitalized, it was considered necessary to expand the range of the station area, improve public contribution plans, and improve the ratio of non-residential uses according to the use area, and parking lots. However, it is believed that it is necessary to preserve the regional characteristics and identity of Daejeon, as long as it does not disturb the urban spatial structure and development density system of Daejeon.