Real Estate Prices Research Articles

Some correlations between interest and willingness to pay

This article delves into the causes of the 2008 financial crisis, particularly focusing on its inception marked by the collapse of the mortgage market, which cascaded into the bankruptcy of numerous banks. The crisis stemmed from volatile real estate prices, leading to loans being issued to borrowers without sufficient collateral, exacerbating insolvency. In Hungary, the situation was exacerbated as most housing loans were denominated in Swiss francs, causing repayment obligations to soar due to rapid fluctuations in the exchange rate. State intervention became imperative amidst rising insolvency rates. Motivated by a longstanding interest in the economic ramifications of low interest rates, the authors aim to probe the factors influencing loan repayment motivations. Their research centers on the evolution and functions of state interest rate regulation, while also scrutinizing late fees and interest provisions related to tax and civil law claims to evaluate their impact on timely payments. Embracing an interdisciplinary approach blending historical, economic, and legal perspectives, the article underscores the necessity for well-calibrated regulatory interventions to mitigate the excesses of unrestricted free markets, especially in light of evolving global economic dynamics. It also underscores the importance of prudent regulations, given theories suggesting that persistently low interest rates may lead to economic distortions beyond a certain threshold, while acknowledging the constraints of interest rate adjustments.

Sustainable ceiling panels: exploring the potentials of groundnut shell and sugarcane bagasse-modified plaster of Paris

Plaster of Paris (POP) ceiling has several advantages over other panels but because it is very expensive, only those in the elite class could afford it. Also, groundnut shell (GNS) and sugarcane bagasse (SGB) are recyclable wastes generated in large quantities annually, yet, majorly under-utilized. The persistence of limited/ ineffective waste management systems in less-developed and developing nations warrants adoption of open burning as their prevalent disposal technique. These situations hinder the achievement of sustainable development. Therefore, this research aimed at exploring the potentials of the GNS and SGB in development of sustainable ceiling panels with POP through appraisal of acoustical insulation, heat transfer, and strength properties of the resulting composite panels. Separate samples were prepared by partially replacing the POP with up to 12.3% weight proportions each of the GNS and SGB. The samples were dried, and then characterized/appraised for acoustical insulation, heat transfer, and strength. It was found that SGB-modified POP samples could improve thermal insulation better than those similarly produced with the GNS. With the GNS and SGB contents, the samples were effective by 99.38% and 99.32%, respectively, in maintaining their outlook during their actual service life. The least strength against bending stress was (2.506 ± 0.002) N/mm2 with the GNS content and (2.376 ± 0.002) N/mm2 for using the SGB in the POP matrix, both of which exceeded the minimum requirement for gypsum ceilings (Code H). Further, it was observed that the samples could provide a desirable level of acoustical comfort ceilings in buildings. The undertaking described in this research can avail the building industry with eco-friendly POP-based ceilings that are capable of mitigating the effects of climate change. Focus on influence of fiber treatment and particle sizes is necessary for future research on the fiber- modified POP ceilings.

Impact of the Volume of Developer Housing Units on Real Estate Prices in Poland: Correlation and Coherence Analysis

This article conducts a comprehensive correlation analysis to explore the relationship between the development volume by developers and average market prices in Poland's housing sector from 2010 to 2023, utilizing quarterly data on the number of apartments released and their average prices. It employs both linear and nonlinear correlation analysis alongside wavelet coherence analysis. Preliminary correlational analysis offered insights into the basic interdependency patterns, highlighting how developer-supplied apartment numbers impact average prices. Wavelet coherence analysis, a more sophisticated approach, decomposed the data across various frequencies to uncover complex, nonlinear relationship patterns potentially missed by conventional correlation methods. The findings of the study highlight significant connections between the variables that vary over time and space, emphasizing the complexity of the housing market and underscoring the necessity for thorough analysis. These results carry important implications for developers, investors, and housing policy formulation, contributing significantly to understanding Poland's residential real estate market dynamics and supporting further research and strategic real estate industry planning.

A Real Estate Price Index Forecasting Scheme Based on Online News Sentiment Analysis

The real estate price index serves as a crucial indicator reflecting the operational status of the real estate market in China. However, it often lags until mid-next month, hindering stakeholders from grasping market trends in real time. Moreover, the real estate market has an extremely complex operating mechanism, which makes it difficult to accurately assess the impact of various policy and economic factors on the real estate price index. Therefore, we hope, from the perspective of data science, to explore the emotional fluctuations of the public towards the real estate market and to reveal the dynamic relationship between the real estate price index and online news sentiment. Leveraging massive online news data, we propose a forecasting scheme for the real estate price index that abandons complex policy and economic data dependence and is solely based on common and easily obtainable online news data. This scheme involves crawling historical online real estate news data in China, employing a BERT-based sentiment analysis model to identify news sentiment, and subsequently aggregating the monthly Real Estate Sentiment (RES) index for Chinese cities. Furthermore, we construct a Vector Autoregression (VAR) model using the historical RES index and housing price index to forecast future housing price indices. Extensive empirical research has been conducted in Beijing, Shanghai, Guangzhou, and Shenzhen, China, to explore the dynamic interaction between the RES index and both the new housing price index and the second-hand housing price index. Experimental results showcase the unique features of the proposed RES index in various cities and demonstrate the effectiveness and utility of our proposed forecasting scheme for the real estate price index.

Solution Thermodynamics of l-Glutamic Acid Polymorphs from Finite-Sized Molecular Dynamics Simulations.

Efficiently obtaining atomic-scale thermodynamic parameters characterizing crystallization from solution is key to developing the modeling strategies needed in the quest for digital design strategies for industrial crystallization processes. Based on the thermodynamics of crystal nucleation in confined solutions, we develop a simulation framework to efficiently estimate the solubility and surface tension of organic crystals in solution from a few unbiased molecular dynamics simulations at a reference temperature. We then show that such a result can be extended with minimal computational overhead to capture the solubility curve. This enables an efficient and self-consistent estimate of the solubility and limit of solution stability associated with crystal nucleation in molecular systems from equilibrium molecular dynamics without the need for sophisticated free energy calculations. We apply our analysis to investigate the relative thermodynamic stability and aqueous solubility of the α and β polymorphs of l-glutamic acid. Our analysis enables an efficient appraisal of emergent ensemble properties associated with the thermodynamics of nucleation from solutions against experimental data, demonstrating that while the absolute solubility is still far from being quantitatively captured by an off-the-shelf point charge transferable force field, the relative polymorphic stability and solubility obtained from finite temperature simulation are consistent with the experimentally available information on glutamic acid. We foresee the ability to efficiently obtain solubility information from a limited number of computational experiments as a critical component of high-throughput polymorph screenings.

Forecasting Real Estate Price via Support Vector Machine

This paper focus on the analysis of real estate price using support vector machine. The data set of a local estate price gives the details of the house including the price, date, bedroom numbers and postcode (location), and has been displayed as the line charts. There is comparison about different forecasting methods done by previous researchers including the simple moving average and ARIMA model. The data set is then fed to the model for machine learning and price prediction support vector machine. Moreover, R language is utilised to compute the support vector machine method by e1071 package and graph plotting of different kernel functions inside the support vector machine function. Then the graphs are analysed based on the accuracy of the correct estimation on the diagonal of the matrix generated. Possible improvements including the data collection and survey method are discussed for a higher precision and accuracy result for further study.

Sea level rise risks, adaptation strategies, and real estate prices in Singapore

Sea level rise risks, adaptation strategies, and real estate prices in Singapore

Market Value Appraisal of Virtual Property From A Chinese Legal Perspective

Market Value Appraisal of Virtual Property From A Chinese Legal Perspective

An appraisal of medicinal properties of Choona (CAO) in Unani system of medicine

An appraisal of medicinal properties of Choona (CAO) in Unani system of medicine

Enhancing real estate price prediction using optimized least squares moment balanced machine

Enhancing real estate price prediction using optimized least squares moment balanced machine

이산시간 접근법과 연속시간 접근법에 의한무 재정가격 이론 고찰

The real estate market is characterized by high volatility and intense competition. In particular, investment decision-making in real estate development needs to be more flexible in response to uncertain environmental changes, including legal and economic factors. No Arbitrage Pricing Theory is a framework that determines the prices of financial derivatives based on the assumption that no arbitrage opportunities exist in the financial market. This theory requires a pricing structure that eliminates risk-free opportunities for guaranteed profits across various financial products. The objective of this study is to compare the differences between discrete-time and continuous-time approaches within the No Arbitrage Pricing Theory, which is one of the pricing theories in financial engineering. Specifically, this research aims to examine the concept of no arbitrage, the Markov properties of each model, and the replicability of the payoffs of complete options from a practical perspective. By analyzing No Arbitrage Pricing Theory, this study explores methods for assessing uncertainty in real estate prices from the viewpoint of financial engineering, including methods for evaluating real estate returns, risk analysis techniques, rental derivative valuation methods, and real estate option valuation approaches. Building on this research, various analytical methods are expected to develop, and this study may play a fundamental role in that advancement.

거시경제변수가 수도권 및 비수도권 주택가격에 미치는 요인분석

Korea's real estate policy has been preparing real estate measures only with supply and demand. It is necessary to study the changes in real estate prices of macroscopic economic variables. This is because the real estate market is complex and affected by various external factors. Unlike previous studies, this study analyzed the effects of macroeconomic variables on apartment houses in the metropolitan area and apartment houses in the non-metropolitan area, and the results of the study using the cointegration test, Granger causal variables, and impact response analysis are as follows. In the case of houses in the metropolitan and non-metropolitan areas, the amount of money and the housing jeonse price index, which are macroscopic economic variables, are commonly affected, but there is a difference in the magnitude and timing of their influence. Mortgage interest rates and loan amounts initially had a negative effect, but it was concluded that even if interest rates and loan amounts increased over time, the actual transaction price index of houses in the metropolitan area rose. The real transaction price index of houses in the non-metropolitan area was also found to have a close effect on the mortgage interest rate and average income per household.

A Study of the Quantitative Impact of Different Credit Policies on Real Estate Price Volatility in the Housing Finance Market

Real estate industry is an important industry of national economy in any country, which constitutes an important element of the wealth of the whole society and has a stable and long-term impact on national economy and social development. Based on the price theory of supply-demand equilibrium and the theory of real estate cycle fluctuation, the article combines the stock-flow model and vector autoregression model of real estate price to establish a quantitative analysis PVAR model of credit policy on real estate price fluctuation. Data from 31 provincial regions in China from 1998 to 2023 are selected as the research samples, and the specific effects of different credit policies on real estate price volatility in the housing finance market are verified through empirical analysis. When the scale of credit policy expands by 1 percentage point, real estate prices will increase by 0.00611 percentage points, while credit interest rates have a positive impact on real estate price volatility for 6 to 7 periods, and then change to a negative impact. Different regions under the credit policy on real estate price volatility there are differences, and the faster the growth rate of real estate prices in the region credit policy on real estate prices to promote the role of the more obvious. Real estate price regulation needs to accurately combine regional and economic development, so as to optimize the credit policy of the housing finance market, and then ensure the stability of real estate prices.

The Importance of Residential Real Estate Characteristics in the Assessment of Selected Groups of Real Estate Market Participants

Abstract The price and market value of real estate is influenced by a number of attributes of an economic, legal, physical-technical and environmental nature. Some market characteristics, such as location, neighborhood or area, have a universal dimension, while others result from the specifics of the analyzed real estate segment. The purpose of the survey was to assess the importance of residential real estate characteristics by real estate market participants. Two groups were surveyed. The first was made up of potential buyers and the second was made up of professionals associated with the real estate market (including appraisers, real estate agents). The results obtained were subjected to comparative and selected cross-analysis and discussion.

Research on Population Aging, Effectiveness of Monetary Policy, and Real Estate Prices

As the global trend of population aging intensifies, the multifaceted impacts of aging on the economy are becoming increasingly apparent, especially in the areas of monetary policy and the real estate market. This paper aims to explore the intrinsic relationship between population aging, the effectiveness of monetary policy, and real estate prices. By analyzing the impact of demographic changes on consumption, savings, and investment behavior, the paper further reveals how aging affects the transmission mechanism of monetary policy and examines the role of monetary policy in real estate price fluctuations. The paper begins by defining the current status of population aging and discussing its broad economic impacts, followed by an analysis of the tools and transmission mechanisms of monetary policy and its specific effects on the real estate market. Based on the construction of a theoretical model, this paper also empirically tests the responses of the real estate market to monetary policy in different countries and regions facing aging societies. Finally, based on both theoretical and empirical research results, policy recommendations are made regarding the adjustment of monetary policy and regulation of the real estate market in the context of an aging society. This study provides theoretical support and practical guidance for policymakers to address the challenges that population aging brings to economic policy.

Корреляционно-регрессионный анализ как инструмент прогнозирования прибыли организации

The planning process is important for the rhythmic functioning of an organization. Planning at each stage of the production process leads to forecasting financial results, in particular the profit from sales of the organization. As a rule, in the process of forecasting profit from sales, only internal factors or processes that influence the organization’s activities are taken into account. The purpose of this paper is to substantiate the use of correlation and regression analysis as a tool for forecasting profits from a sales organization. The paper considers two approaches to forecasting profit from sales, taking into account, among other things, the influence of external factors affecting organizations whose main activity is related to the rental and management of its own or leased real estate. The first approach to profit forecasting is based on the direct counting method and economic and mathematical algorithms. The second approach is the use of correlation and regression analysis, based on the influence of selected external factors. Profit forecasting using correlation-regression analysis showed that the average rental price of commercial real estate has a negative relationship with sales profit, and the amount of electricity consumed has a positive relationship with profit from sales. Based on the results of the correlation analysis, it can be concluded that changes in the rental price of commercial real estate and the volume of electricity consumed affect the company’s sales profit. The value of the forecast sales profit obtained under the optimistic scenario, calculated by the direct calculation method, is close to the forecast values calculated by the methods of correlation and regression analysis, which proves the importance of using this forecasting tool.

WEB SERVICE MANAGEMENT SYSTEM FOR PREDICTING REAL ESTATE PRICES USING MACHINE LEARNING TECHNIQUES

Today there are many different web services for renting real estate, but none of them provides price forecasting capabilities. There is a need to create a platform that allows users to receive accurate real estate price forecasts with minimal time expenditures. The aim of this paper is to develop the architecture of a web service for real estate price forecasting, considering various apartment characteristics. We have prepared a review and analysis of existing analogues of real estate rental web services, functional and non-functional requirements for a web service for apartment price forecasting. The high-level architecture and technical tasks for the participants of our web service were also developed and described in our research. The paper proposes the development of a web service that predicts real estate prices based on various property characteristics. The key objectives are: analyze existing real estate rental web services and identify functional gaps, particularly the lack of price prediction capabilities; establish technical requirements for a comprehensive web service that unifies tenants, landlords, and administrators to facilitate informed decision-making; utilize machine learning techniques, such as linear regression, random forest, and decision trees, to develop a price forecasting module within the web service; evaluate the performance of different machine learning models using RMSE metric. The paper presents the high-level architecture of the web service, including modules for user registration, data validation, apartment search and interaction, and price forecasting. The experimental results demonstrate that the random forest model outperforms linear regression and decision trees in predicting apartment rental prices in Kyiv. Overall, the study highlights the potential of integrating machine learning into real estate web services to enhance transparency and informed decision-making for both tenants and landlords.

Exploring Internal Factors that Affect Real Estates Prices: Evidence from Singapore

The purpose of this paper is to analyze the internal factors that generate the difference in real estate prices in Singapore. The objectives are to achieve a deeper understanding of the reason why prices of real estate are different at the same period and to provide a standard for investors to detect real estate with prices that are underestimated and consider whether to invest or not. This paper examines intrinsic factors that mainly affect real estate prices. Regression analysis is the core methodology to analyze the factors; the author used normalization to process the data. The result shows there is a strong relationship between the price of real estate and house age, distance to the nearest MRT station, the number of convenience stores, and the latitude. It can be discovered that the price of real estate negatively correlates to the house age and the distance to the nearest MRT station; positively corresponds to the number of convenience stores and the latitude.

Research on the Determinants of Real Estate Prices in China under Supply and Demand

Since the reform and opening up, the real estate industry has gradually become the pillar industry of the Chinese people, directly or indirectly promoting the development of macro-economy. However, in recent years, the continuous rise of housing prices has become the focus of attention of the government and all sectors of society, which has triggered extensive and in-depth discussion and attention on the real estate industry. Real estate prices are not only closely related to people's lives, but also related to the development of cities and the long-term stability of the country. The healthy development of the real estate industry is also related to the composition of household wealth and government revenue, as well as the stable development of the national economy. Therefore, it is of great practical significance to pay attention to the changes of housing prices. This paper mainly summarizes and reviews the relevant literature on the analysis of factors affecting house prices at home and abroad from three aspects: the supply side, the demand side and the hedonic price model of real estate. It provides basic underlying logic for academic research, policy-making, practical application and individual house purchase decision-making.

Research on the Influencing Factors of House Prices in Sydney

Considering various factors that affect house prices, this study investigates whether housing prices in Sydney can maintain their value or even increase in the future. A multiple linear regression model was applied using a dataset obtained from Kaggle, which includes 11,161 real estate transactions in Sydney from 2016 to 2021. This paper uses it to explore the relationship between housing prices and 12 independent variables (originally 14 but some have to be abandoned), such as the number of bedrooms, property size, median suburban income, and distance to the central business district (CBD). The results indicate that some factors have a positive, negative, or no impact on house prices and that 39.6% of the real estate price is influenced by them, indicating a moderate level of predictability. These findings provide valuable insights for investors and policymakers interested in the dynamics of the Sydney real estate market, emphasizing the importance of geographic location and property attributes in determining housing prices.