Visualization-Aided Exploration of the Real Estate Data

Shenzhen is one of cities that is real estate market-oriented, and the real estate is an important industry in the economic development. Real estate price statistics have drawn more and more attention and is being questioned due to distortion of announced real estate price in recent years. For example, the new commercial house price statistics is measured by using a simple average of price statistics, but the sample's heterogeneity leads to the house prices by statistics inconsistent with the real house prices. Meanwhile, in the process of second-hand housing transactions, the transaction prices is distortion and real house prices are out of control, caused by fake contracts. In order to grasp changes and trends of the price in the real estate market accurately, this research designs a house price indexes model, which covers new and secondary house in Shenzhen based on the real estate database. The Shenzhen house price indexes system employs B/S and three-layer architecture. It is based on the Skyline platform, and utilizes the JavaScript language for secondary development. The Shenzhen house price indexes system can effectively integrate real estate data, spatial data, 3D simulation model and house price indexes model, which achieves to monitor, manage, and analyze real estate data, and house price indexes. The system prepares and publishes periodic house price indexes, which could provide the latest, timeliest, and most accurate real estate price information for the parties in the real estate market.

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.

paper has two purposes. First to describe qualitatively and quantitatively the current state of residential real estate marketing in Moscow and Kiev and to compare that to current practices in Philadelphia. Second, to surveys comparative rents and values to compare current conditions in Moscow to Philadelphia and to further compare current conditions in those cities to similar data from each city ten years ago. This paper updates work originally published by the lead author in England in 1996 (see Factors Influencing Residential Real Estate Rents and Values in Kiev, Ukraine when Compared to Philadelphia, with L.S. Kyj, and M.J. Kyj, Journal of Property Finance, Vol. 7, No. 2, pp. 53-70, November 1996.); and also updates work published in Russia in 1999 (see Marshall, P., T. Belkina and M. Kyj (1999). Is It Profitable to Invest in Rentable Residential Real Estate in Moscow? Journal of Problems of Theory and Practice in Reforms of Regional Economies (a journal of the Institute of Real Estate Research of the Russian Academy of Sciences), (in Russian), pp. 178-186.) We are unaware of any comparable work and believe that a comparison of condition in 2005 versus those a decade earlier, and not long after the fall of the U.S.S.R., would be useful to document in the literature. Data collected for 2005 primarily results from sorting and summarizing real estate data available on the websites of important real estate companies in each city. Normal statistical analysis is performed on this data to reach meaningful conclusions about current market conditions in each city. Data quality is compared and contrasted to that available in the earlier referenced works. Some tentative key conclusions appear to be: * Philadelphia has seen dramatic increases in the prices of real estate, but that is not surprising considering how well documented the American (coastal) real estate boom has been; * Moscow real estate price have also exploded, but perhaps as much from the development of much grander properties as from pure price appreciation, particularly in suburban areas; * Central Moscow properties are and have continued to be substantially more expensive (as measured by both value and rent) than similar properties in Philadelphia. Undoubtedly this is related to the fact that Moscow is the national capital. As such it is a magnet unlike anything we have in America. Many Russian live very well in Moscow. Few live very elsewhere. Such demand pressure means higher real estate prices and rents; * Real estate marketing has dramatically improved in both Kiev and Moscow compared to both a decade ago and relatively versus U.S. real estate marketing. Other than the lack of an MLS, this process has probably reached its limit.

The characteristic of the residential real estate asset class is low liquidity due to low liquidity of the residential market. While in the stock market, the current prices and bid/ask spread are known and easy to calculate, in the residential real estate market, this information can be measured on the basis of prior transactions, but unlike the securities, residential properties do not always have the same characteristics. In addition, in the stock market, there are daily data while real estate data may be weekly or monthly because, for the real estate market, high intra-year data are not frequently available. Moreover, the residential real estate prices, unlike stock prices that fluctuate more or less randomly, tend to follow a clear trend (increasing or decreasing), and price are sticky. Sometimes, price stickiness can go down when sellers tend to resist selling because they do not receive the offer desired, and they withdraw their house from the market.KeywordsReal EstateHouse PriceHousing MarketEuropean Central BankReal Estate MarketThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

We examine the effect of forward sale (pre-sale) activities on the volatility of spot prices in the real estate market. The abundance of pre-sales data and major changes in regulatory control on the pre-sale market during the 90's in Hong Kong allow us to undertake empirical tests using Hong Kong's real estate data. Our results show that the volatility of spot prices increased significantly after forward sales were severely dampened by regulatory control measures introduced in 1994, but decreased again when the measures were partly relaxed in 1998. The results contribute to the long lasting debate on whether the introduction of a futures market reduces the volatility of spot prices. Previous studies were mainly conducted in markets with low transaction costs, notably financial markets. By utilizing the unique regulatory changes in the pre-sale market of Hong Kong, we are able to conduct an experiment on the conditional volatility of spot prices in a high information-cost environment, thereby shedding light on the important role of forward housing contracts in providing price expectation information for spot trading.

The majority of present-day aggregators of the Russian real estate market fail to meet modern requirements and trends. Currently, the information support of real estate valuation activities is generally assigned to individual values, rather than the valuation community as a whole. The absence, insufficiency, unreliability of the market information makes it difficult to determine the cadastral value of the properties as well fulfill the requirements prescribed by the existing methodological recommendations. The main problem in developing digital platforms is that the value of the digital platform proposition can only be adequately assessed by the platform developer, while the complexity of creating a quality digital platform may exceed the benefits for the developer. In this regard, we set a goal to analyze the aggregators of the real estate market in terms of the shortcomings, whose systematization is to lay the basis for a consolidated real estate market database of Russian Federation. In this article, selected digital platforms were analyzed with regards to meeting specified criteria using a multi-criterial rating system. The results of the study represent the necessary criteria, conclusions concerning compliance with certain criteria based on the analysis of classifieds, as well as recommendations for the use of innovative technologies to ensure the most advanced operation of real estate market aggregators. In addition, several forms of information exchange between market players and viable solutions to common problems are suggested. A method of developing a consolidated real estate market database was developed. It is based on innovative information technologies and the actionable way of implementing software solutions designated for automation and structuring of real estate data.

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

At present, the importance of green finance in the world market is becoming increasingly prominent. Various kinds of green products have emerged, and auxiliary policies have also come into being. China is one of the world's largest carbon dioxide emissions and the world's largest investor in new energy. In the face of severe environmental problems and challenges of sustainable development, the Chinese government attaches great importance to the development of green finance, and has adopted a series of positive policy documents and guidance, including the establishment of special funds, providing preferential fiscal and tax policies, and the establishment of a green bond market to promote its role in the financial system. In addition, China has set up a set of green financial standards and evaluation system to guide financial institutions and enterprises to be more transparent and sustainable in environmental risk management and green investment.
 The emergence of green finance has brought a certain impact on the real estate industry, a pillar industry of China's economy. At present, many people in China, especially in some central cities, have speculated on housing prices. This behavior has led to a speculative increase in the real estate market, a rapid rise in housing prices, an excessive consumption of resources, and the aggravation of social inequality. This conflicts the concept of sustainable development and environmental friendliness advocated by green finance. At the same time, the investment in green finance will squeeze out a part of the real estate market share. By directing investment in real estate to the flow of green buildings and energy conservation technologies, green finance can also promote the efficient use of resources and sustainable environmental development.
 Therefore, it is of great significance to deeply study the impact of green finance on real estate. By discussing the application of green finance in the real estate field, effective ways can be found to reduce the consumption of resources in the real estate industry, improve the energy efficiency of buildings, promote the development of green buildings, and guide China's real estate market towards the direction of sustainable development in practice. This is of great practical significance for realizing the dual goal of economic growth and environmental protection.
 Previous scholars have discussed a wide range of factors affecting real estate, including the monetary system, consumption level and so on. The related discussion on green finance also focuses on the impact of the ecological environment and some green products on the financial market. No scholar has specifically discussed the impact of green finance on real estate as a whole.
 Therefore, in order to ensure that the regression results are not affected by the economic turbulence during the epidemic period, this paper selects the relevant data of provinces and cities from 2008 to 2020 to regression the fixed effect model panel, conducts a study on the real estate price, and draws the following basic conclusions: green finance has a negative impact on the real estate price.
 The marginal contribution of this paper is the use of various green coefficient to build green financial index, to explore the influence of green finance as a whole, and make up the green finance influence the lack of the real estate research, to solve the current real estate investment market proportion and rising prices put forward new slow problems.
 The chapters of this paper are arranged as follows: the second part is the literature review, the third part is the experimental design and data, the fourth part is the empirical results, the fifth part is the robustness test, and the sixth part is the conclusion and policy suggestions.

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.

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.

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.

Real Estate Allocation Puzzle in the Mixed-Asset Portfolio: Fact or Fiction?

This paper explores the effect of the herd mentality on consumers’ buying decisions in the real estate industry, which is highly influenced by social media influences. It helps to understand how social media influencers shape consumer behavior and decision-making processes in the economy of real estate transactions. Social media influencers significantly impact herd mentality, leading to market jumps and potential risks in the real estate area. The paper also contributes to the debates on the role of social media in shaping consumer behavior and its connection with the real estate industry. This paper provides awareness for researchers seeking to understand and navigate the real estate market dynamics in the digital age and fast-changing economy today. Using social media in this industry today is indeed a significant determinant of essentially making your clients, your fans. The way homes and agents are perceived on social media is crucial to the success of the industry.

In recent years, the heat of the real estate market has decreased, and the price of real estate in a few places has declined slightly, mainly due to the effect of the purchase restriction policy and the slowing down of economic growth. Is it difficult to develop the real estate industry in the future? Will the real estate market be depressed for a long time? For example, some people think that the real estate cannot be sold, and the price must be greatly reduced? I believe that the real estate industry will continue to develop. It is only the subjective desire of some people that the long-term price reduction of real estate will not rise. This desire is neither in the interests of the state nor in the interests of society, nor in line with the laws of the socialist market economy. Based on the comprehensive analysis of various factors, China's real estate industry will continue to develop, the real estate market will be more sound, and the real estate price will definitely not fall all the time, and will certainly "rise steadily".