Exploring property orientation preferences and local variations

PurposeThis study aims to empirically examine the relationship between real estate and stock market of Pakistan.Design/methodology/approachThe data of two real estate indices (house price index and plot price index) are taken for the Pakistan and its four big cities, i.e. Lahore, Karachi, Rawalpindi and Islamabad. It estimates the integration between series by applying the Johansen cointegration test. Moreover, the vector error correction model is applied to examine the short and long-run causal relationships between series.FindingsThe findings show that the real estate markets are cointegrated with the stock market. They imply that the real estate and stock markets are good substitutes in investment allocation, but investors cannot get the benefit of diversification by making a portfolio of real estate and stock markets in Pakistan. Moreover, the long-run causality is observed from majority house markets to the stock market, whereas short-run causality is evident from majority plot markets to the stock market. Hence, the real estate market leads the stock market in the short run and long run, suggesting the credit-price effect in the majority of real estate markets in Pakistan. These causality results are helpful for investors in the forecasting of real estate and stock markets in Pakistan.Research limitations/implicationsThe limitation of the study is the lower number of observations (107), because house and land prices are only available in monthly frequency from January 2011 in Pakistan.Originality/valueTo the best of the authors’ knowledge, no researcher has investigated the real estate and stock market nexus in Pakistan. Therefore, this study focuses on examining the relationship between the real estate and stock market of Pakistan. The link between real estate and stock markets will provide useful insights to the portfolio managers, real estate companies, property agents, stockbrokers and investors.

This study attempted to present discussions and directions regarding artificial intelligence(AI) and the prevention of corruption in Korean real estate. This Paper dealt with moral hazard, corruption prevention, and trust using real estate, the 4th industrial revolution(4IR), AI, and intelligent information technology as keywords. Prior research is focusing on economic utility and technical benefits related to AI and real estate. In contrast, this study pointed out the risk of moral hazard and corruption arising from the combination of AI and real estate, and conducted a paper to prevent moral hazard and corruption. In this respect, there is a difference between this study and existing previous studies. This is where the originality of this study appears. The results of this study on anti-corruption of AI and Korean real estate are as follows. As a result of the study, the problems of AI and Korean real estate, and anti-corruption were presented in three major categories: 1. AI and the problem of changing times, 2. Korean real estate and corruption problem, and 3. AI and real estate problem. In relation to this, this study largely derived three measures as implications of the research. The contents include, first, responses to the issues of AI and changing times, second, responses to Korean real estate and corruption issues, and third, measures to respond to AI and real estate issues. This study hopes to restore market trust through Artificial Intelligence(AI) and the prevention of corruption in Korean real estate, and to build a future where the benefits and utility provided by real estate, AI, and intelligent information technology are desirable.

This study examines whether property transaction affects the price discovery process in real estate markets. Prior literature shows that price discovery generally first takes place in the securitized public real estate investment trust (REIT) market. We conjecture that property transaction provides novel information to the direct real estate market and can change the dynamics between public and private real estate returns. We employ a unique dataset of property transactions to construct "transaction windows¨ and specifically examine the causality between public and private real estate markets around these periods. We form firm-level pairs of public and private price series, and estimate the normalized common factor loadings per Gonzalo and Granger (1995) by using a vector error-correction model. Our findings show that a significant proportion of price discovery happens in the private market instead of the public REIT market. Our results are robust to investments of different property types and different lengths of transaction windows. Overall, the findings in this study imply that property acquisition and disposition provide crucial information to the private real estate market and induce a reverse causality between the public and private markets.

Chemical mass balance source apportionment of PM 10 and TSP in residential and industrial sites of an urban region of Kolkata, India

The paper presents theoretical and methodological approaches to forecasting pricing in the real estate market based on the study of stochastic models of time series, which are statistical samples of prices of real estate in certain segments of this market generated consistently over time. The main stages of the research of time series in the real estate market are given. Two main components of time series of real estate prices are distinguished - fundamental (regular) and random (irregular). The fundamental (regular) component of the time series contains trend, cyclical and seasonal trends. The random component is noise, which slightly deviates the value of the time series from the trend. The work analyzed the fundamental price-forming factors in the real estate market, which have the greatest influence on the formation of the real estate price and determine its trend. The results of the analysis of time series of prices on the secondary real estate market in the traditionally most active business cities of Ukraine (Kyiv, Kharkiv, Dnipro, Odesa, Lviv, Ivano-Frankivsk) in the period 2016-2023 are presented. It is shown that prices on regional real estate markets are formed under the influence of both general trends throughout the country and random crisis phenomena inherent in this region. In the regional section of the analyzed real estate markets, on average, a growing trend is observed, which is stimulated by the increasing solvent demand for residential square meters. At the same time, it is of particular interest to study the behavior of real estate market prices during periods of crises caused by extraordinary events and conditions (pandemic and war) observed in 2020, 2022 and in the current year 2023. If the coronavirus, as a crisis phenomenon, affected the dynamics of regional real estate markets more or less equally, then the impact of a full-scale war manifested itself differently in different regions of our country. In front-line cities that suffer quite a lot from shelling (for example, Kharkiv), the real estate market reacted by lowering prices and demand. At the same time, in the western regions (for example, Lviv, Ivano-Frankivsk), the cost of square meters has increased significantly, which is due to a sharp increase in demand due to temporarily displaced and evacuated persons. Further directions of time series research for forecasting pricing in the real estate market have been determined.

The paper is focused on the use of fuzzy numbers in some different spheres n real estate researches, investments and valuation. The real estate market is not perfect and it is subject of many external influences, such as: an economic situation in the country, seasonal activity, availability of financial assets. Therefore it doesnit have precise logic connections and definitions. That is why it is necessary to use fuzzy numbers when we research a real estate market. The process of estimation of real estate investments of any country considerably becomes simpler. In paper is analyzed the real estate market of Minsk n the capital of Belarus. It was used fuzzy numbers. At the second part of paper it will be shown that fuzzy numbers allows us to get model for the property valuation.

The purpose of the study is to determine the impact of the full-scale invasion of the Ukrainian real estate market in terms of assessing the supply of commercial and non-commercial real estate, taking into account changes in construction activity, price levels and the transformation of housing demand. The study aims to identify the main trends and factors that have contributed to the transformation of supply in the real estate market, as well as to outline opportunities for its stabilization and development in the post-conflict recovery. Design/methodology/approach – The research employs a thorough and systematic methodological framework, integrating a broad literature review with qualitative and quantitative analyses. The paper applies a systematic approach, which made it possible to study the real estate market as a complex system, taking into account the interaction of various economic factors; dialectical logic, induction and deduction are used to analyse the prospects for strategic development of the real estate market; comparative and statistical analysis is used to assess the dynamics of housing prices and construction volumes; the problem-oriented method is used to identify key problems that impede the stabilisation of the real estate market in the context of war; index method – for assessing the level of concentration of the primary real estate market in Ukraine in numerical terms.SWOT analysis, which is used to. Findings – Trends in the development of the real estate market before and during the full-scale invasion of Ukraine were studied. An analysis of the areas of residential and non-residential buildings at the beginning of construction and put into operation in 2021–2023 was carried out. Factors affecting the transformation of demand for real estate due to the impact of the war are analyzed. Price changes on the primary and secondary real estate market in safe regions in 2021–2023 were analyzed. Using the Herfindahl-Hirschman and Lind indices, the market shares of developers in the primary real estate market of Ukraine in 2023–2024 were estimated, an analysis of market concentration was conducted, as a result of which the main market participants in both years were identified. SWOT-analysis was used to identify internal strengths and weaknesses, as well as external factors influencing the real estate market of Ukraine under economic instability. Practical significance – The research provides valuable insights for companies aiming to enhance their operational performance in the real estate market in the post-war recovery period. The study offers a comprehensive framework for understanding how the identified factors influencing the development environment of real estate market participants can be strategically used to improve customer experience and optimise internal processes, ensuring sustainable competitiveness in the long term. Originality/value – This study contributes to the existing literature by offering an in-depth analysis of the factors influencing the real estate market in the context of economic instability. The study emphasises the importance of the significant transformations in the structure of supply, demand and prices in the primary and secondary residential and non-residential real estate markets, taking into account the differentiation of these changes by regions where the level of security and affordability of housing varies significantly. The above outlines specific approaches to the adaptation of the real estate market in the crisis. The conclusions highlight the prospects for Ukrainian real estate market participants to attract international grants and finance innovative construction projects, as well as to develop environmentally friendly and autonomous solutions, diversify their business lines, focus on safe regions, and expand cooperation with international partners.

Where will China's real estate market go under the economy's new normal?

This paper empirically investigates the heterogeneous impacts of the media sentiment about policies with different themes on the real estate market in China. Based on the policy texts collected from both official and unofficial sources, we construct sentiment indices to capture the sentiment about policies with different themes, including real estate policies, fiscal policies, monetary policies, land policies, healthcare policies, household registration policies, and education policies, using text mining methods. Mediation models and GARCH models are then established to examine the impact of these sentiment indices on the real estate market. The E-GARCH model is established to examine the asymmetric effect of positive and negative sentiment on real estate market. The results show the following: (1) The real estate market in China is more affected by the policy sentiment on official media compared with the unofficial ones. (2) Policy sentiment affects the real estate price through the mediating variables of interest rate, real estate construction area, and real estate sales. (3) The impacts of sentiment with different themes on the volatility of the real estate market are heterogeneous. (4) The impacts of policy sentiment on official media are more pronounced in a tight government-policy environment than those in a loose one. (5) The effect of negative unofficial media policies sentiment on real estate price is bigger than the positive unofficial media policies sentiment.

This paper is concentrated on analyzing the CEE countries¥ housing and lending market development in 2002-2009. Some of the CEE countries (Bulgaria, Latvia, Lithuania, Romania, and Slovakia) have experienced 250-400% price increases at the peak of the real estate boom in 2007-2008. Other countries in the region (Hungary, Slovenia, Czech Republic, Poland, and Estonia) have had less extensive price increases between 160% and 250%. By the time of 2009 and early 2010 the real estate prices have declined in the region mostly 10-30% from the peak. Most of the CEE countries experienced 50-90% annual increase in housing debt, which by 2009 was highest in Estonia and Latvia (42.6% and 35.4% of GDP). For example Estoniaís housing debt to GDP is above the euro zone average (38% in 2008, ECB). The second group is Czech Republic and Lithuania ñ they both have approximately 21% housing debt to GDP ratio and the others have less than 15%. The aim of this paper is to understand: which of these countries have the greatest potential for housing market recovery? I have conducted comparable analysis of these countries and their real estate and lending market. I have collected data from central banks, national statistic offices and real estate companies. The results suggest that countries with higher housing debt had stronger real estate booms and the current bust-cycle has caused much steeper decline in prices. The outstanding housing debt in 2009 is in some cases 2-5% smaller than the previous year and only in Slovenia, Slovakia, Czech Republic and Poland have seen 6.75-10.59% increase in housing loans. The total of outstanding loans may have been decreasing or slightly increasing for some countries, but overall housing loans to GDP ratio has been growing even after the recession started, because the GDP of the CEE countries (exception is Slovakiaís forecast for 2009, Eurostat) has contracted. This has had paralyzing effect on the demand for homes and therefore has caused the steep fall in real estate prices. It has to be mentioned that big part of lending has been made in Euros and some of the currencies, which are not pegged to Euro (CZK, HUF, PLN, RON), have lost 14-33% of their value comparing to Euro (ECB). This means that with falling GDP and rising unemployment, many lenders have to pay their Euro-based loans with weak currency. As a conclusion it can be stated that real estate markets in countries with similar macroeconomic conditions, that are less exposed to housing loans have highest potential for faster recovery in housing market.

Urban crime and crime prevention measures can influence perceptions of safety, neighborhood desirability, and real estate dynamics in urban areas. Through a Systematic Literature Review (SLR) and meta-analysis, this study analyzes the influence of the aforementioned factors on real estate values. To this end, 50 peer-reviewed publications indexed by Scopus, Web of Science, and Google Scholar were selected according to defined eligibility criteria. The aim of the study is to answer research questions concerning the impact on property values of the following phenomena: 1) urban insecurity; 2) traditional and social crime prevention measures; 3) Crime Prevention Through Environmental Design (CPTED); 4) CPTED applied to urban green spaces. The results show that urban crime, in the absence of adequate prevention measures, has a negative impact on property values, with devaluations of up to 12–19% depending on the context. Although traditional and social crime prevention measures show positive effects on property values, the available studies are still limited. The CPTED approach also appears to have a significant impact on the real estate market, but research in this area is lacking, particularly in quantitative terms, and is even scarcer when applied to urban green areas. The SLR reveals theoretical, operational, and social implications regarding the relationship between urban crime, prevention strategies, and real estate dynamics, which are useful for researchers, urban planners, and real estate players.

The forecasting of real estate markets continues to be a topical issue and has attracted several research interests over the decades. This is because the real estate market is a major constituent of both the local and international economies and is not insulated from the swings that characterize the economies. Professional real estate valuers interpret market information as they affect real estate investment and aid the real estate investor in decision making. Therefore, real estate market forecast is an important tool for investment decision making. In Nigeria, however, real estate market forecast and its underlying factors are largely unexplored. This neglect is quite obvious given the data challenges and characteristics of the real estate markets. In view of the foregoing, this study is an attempt to investigate the complexities inherent in the conduct of commercial real estate market forecasts, methods adopted by Nigerian Estate Surveyors and Valuers in predicting the real estate markets and goes further to examine the wider implications of real estate market forecasts to commercial real estate investment in Africa’s most populous and largest economy. The study carried out a questionnaire survey involving Estate Surveyors and Valuers in Lagos megacity, Nigeria’s commercial capital. Preliminary results indicated that the availability of market data was on the increase but the Nigerian professionals are yet to fully incorporate forecasting models in their valuations. The paper recommended that international real estate organizations and investors should collaborate with indigenous researchers, practitioners and the Nigerian real estate professional regulatory bodies in order to develop a sustainable market based forecasting framework.

The main function of the property manager is to realize the property owner's objectives, to generate income for them as well as maintain or increase the value of the investment property. The job of a property manager is complex and requires expertise in a more comprehensive field of real estate. The real estate market which has a direct impact on the job of real estate managers, is a major component of the economy in general. The two are very close where economic performance can cause changes in the real estate market. In order to assess the current and future potential of a particular property and then develop a management plan for it, a property manager must first understand the underlying economic trends and their implications for the real estate market. Only then can the property manager proceed with the steps to develop an efficient and professional management plan. All the activities involved in the marketing of the space to be rented are directed towards a single goal, which is to sign a rental agreement between the owner and the tenant. The role of the property manager in rental management will involve aspects of rent collection, establishing good relations between owners and tenants, coordination between tenant contractors, management of utilities and building services and monitoring the return of the premises to a good condition after the termination or expiration of the rental period.

With the acceleration of global urbanization, the real estate market plays a vital role in urban development. However, the emergence of real estate bubble has become a global problem, which poses a serious threat to economic and social stability. The purpose of this study is to explore the key factors of real estate bubble formation in the process of urbanization, so as to reveal its complex mechanism and influence. It is found that financial factors are one of the main driving forces for the formation of the real estate bubble. Low interest rate policy, loose monetary policy and easy access to credit conditions have stimulated investors' funds to flow into the real estate market, which has led to unreasonable price increases. The relationship between supply and demand also played an important role in the process of bubble formation. In addition, speculation has also played a key role in the real estate market. Investors usually pursue quick profits, but the volatility of the real estate market attracts speculative investment and promotes the unreasonable rise of housing prices. The government's land policy, planning restrictions and development control also have a direct impact on the market, and government policies and measures can affect land supply and real estate market prices. Generally speaking, the formation of real estate bubble in the process of urbanization is a complex phenomenon of multi-factor interaction, and its mechanism involves finance, supply and demand, speculation, policy and external factors. This study is expected to provide powerful theoretical basis and policy suggestions for decision makers to deal with the real estate bubble in the process of urbanization.

The explanation of the price and value of real estate is still a big challenge that limits forecasting the market value, and explaining bubbles and crashes in the real estate market. Thus, this paper extends the theory of market equilibrium to redefine the value concept for explaining market behavior in real estate. The demand–supply analysis is used to define market value and value distribution between sellers and buyers in the real estate market. From this base, the roles of investors and credit institutions are identified in promoting real estate activities, and also in causing bubbles and crashes in the real estate market. Moreover, numerical experiments are used to conduct market behavior in typical situations with defined demand and supply functions in the real estate market. The research results and findings provide key implications for providers, investors, customers, credit institutions, and governance in the real estate industry. The paper also contributes to the theoretical foundations for further research on market behavior and valuation in real estate.