An Effective Prediction Method for Supporting Decision Making in Real Estate Area Selection

Real estate has been a dominant industry in many countries. One problem for real estate companies is determining the most valuable area before starting a new project. Previous studies on this issue mainly focused on market needs and economic prospects, ignoring the impact of natural disasters. We observe that natural disasters are important for real estate area selection because they will introduce considerable losses to real estate enterprises. Following this observation, we first develop a self-defined new indicator named Average Loss Ratio to predict the losses caused by natural disasters in an area. Then, we adopt the existing ARIMA model to predict the Average Loss Ratio of an area. After that, we propose to integrate the TOPSIS model and the Grey Prediction Model to rank the recommendation levels for candidate areas, thereby assisting real estate companies in their decision-making process. We conduct experiments on real datasets to validate our proposal, and the results suggest the effectiveness of the proposed method.

Analysis on the Competitiveness of Industrial Real Estate Based on Diamond Model ——Taking Jiangxi Province as an Example

Based on the current development of industrial real estate of Jiangxi Province, comprehensive analysis was conducted to 6 aspects using the idea of diamond model, namely, factor conditions, demand conditions, related and supporting industries, firm strategy, structure and rivalry, government and chance through the comparison with the other industrial real estates and analysis on horizontal competition in China. Development countermeasures were investigated to improve the competitiveness of the industrial real estate in Guangxi, putting forward the strategies of developing the role of government and business, seizing the opportunities of the times, city-industry integration development, implementation of industrial integration and integration of city development, the implementation of industrial integration and investment planning.

Cyclical fluctuations of real estate investment across regions in urban China

Taking the concrete conditions of each region into full consideration, the Chinese real estate market is divided into an eastern market, a central market and a western market. For each market, the autoregressive integrated moving average （ARMA） model is established and spectral analysis is carded out to better understand the changes in the real estate markets in each region. The results show the investment levels and several kinds of cycles within each market. The level of real estate investment （LREI）in the eastern region is the higiaest, and the short cycle of investment is about 4 to 5 years; the LREI in the central region is in the middle, and the short cycle of investment is about 2 to 3 years; the LREI in the western region has been rapidly increasing in recent years, and the short cycle of investment is about 4 to 5 years. Real estate investment in each area has reached a peak and completed a middle-cycle movement after a period of sustained recession and an upsurge process, which has taken about 9 to 10 years.

China's Real Estate Market: Performance and Prospects

Great changes have taken place in China＇s real estate market during the 12th Five-year Plan period. The real estate market has shifted from being undersupplied to having an equilibrium of supply and demand with regional oversupply. Real estate risks in some cities have begun to emerge, but overall real estate risks remain controllable. Given the major changes taking place in the supply and demand of China＇s real estate market and the current stage of its development, it is projected that China＇s real estate investment growth will continue to slow down during the 13th Five-year Plan period and pressures on the renovation of inventory housing will intensify. According to the changes in the development of China＇s current real estate market, the objectives of real estate development will be adjusted to put a higher premium on the development level, the quality of real estate development, and the improvement of housing conditions. Reform and policy adjustments should be carried out to effectively prevent real estate market risks and to achieve stable operations inside the real estate market itself.

An Intelligent Recommendation System for Real Estate Commodity

Most real estate agents develop new objects by visiting unfamiliar clients, distributing leaflets, or browsing other real estate trading website platforms,whereas consumers often rely on websites to search and compare prices when purchasing real property. In addition to being time consuming, this search processrenders it difficult for agents and consumers to understand the status changes ofobjects. In this study, Python is used to write web crawler and image recognitionprograms to capture object information from the web pages of real estate agents;perform data screening, arranging, and cleaning;compare the text of real estateobject information;as well as integrate and use the convolutional neural networkof a deep learning algorithm to implement image recognition. In this study, dataare acquired from two business-to-consumer real estate agency networks, i.e., theSinyi real estate agent and the Yungching real estate agent, and one consumer-toconsumer real estate agency platform, i.e., the, FiveNineOne real estate agent. Theresults indicate that text mining can reveal the similarities and differences betweenthe objects, list the number of days that the object has been available for sale onthe website, and provide the price fluctuations and fluctuation times during thesales period. In addition, 213,325 object amplification images are used as a database for training using deep learning algorithms, and the maximum image recognition accuracy achieved is 95%. The dynamic recommendation system for realestate objects constructed by combining text mining and image recognition systems enables developers in the real estate industry to understand the differencesbetween their commodities and other businesses in approximately 2 min, as wellas rapidly determine developable objects via comparison results provided by thesystem. Meanwhile, consumers require less time in searching and comparingprices after they have understood the commodity dynamic information, therebyallowing them to use the most efficient approach to purchase real estate objectsof their interest.

Enhancing the Efficiency of the Management of Real Estate Companies through Organizational Structures

The article represents research on ways to make the management of real estate companies more efficient, starting from their structural organization. The organizational structure of the enterprise is a basic component of the general structure of the company and reflects the “anatomy of the enterprise” and the way of its conception, detailing, and implementation directly influence the activities required to achieve the objectives of the enterprise. On closer examination of a series of organizational charts of real estate management companies, the author observed a tendency to use organizational structures that are inefficient. The main objectives of this article are to bring into the practice of real estate management a model of beneficial organizational structure, both for the real estate management companies and for the construction companies that, besides the building activity, also deal with the management of their own real estate, and to emphasize the particular importance of the organizational structure of the company. The appreciation of the organizational structure represents the process of knowledge, understanding, and explanation of the existing situation through a detailed analysis of the factors that influence its condition through: the enterprise development strategy;the type and complexity of services;the size of the enterprise;the quality of human resources;the changes that take place in the external environment of the company, and last but not least, the legal framework and the statutes of the enterprise.

The Applicability of the Maximum Global Utility Method in the Activity of the Real Estate Consulting Service

The real estate sector is vital for ensuring human needs for living and activity. This fact conditioned the formation of a legal, normative and methodological framework specific to real estate, but also the investment attractiveness of the field. Thus, the real estate consultant often solves technical, economic, legal, organizational problems, but the biggest responsibility rests with investment consulting. The applied methodologies, focused on evaluating the efficiency of investments, are not sufficient to propose to the investor a reasoned conclusion regarding the investment priority. In this article, the authors propose a useful tool for real estate consultants in order to reason the decision regarding the investment of funds in commercial real estate for the purpose of renting it out—the Maximum Global Utility (MGU) method. It is proposed to include in the analysis and the way of calculating the indicator “importance level of the real estate” which reflects the criterion of the best use. This indicator ensures the flexibility of the decision depending on the quality characteristics of the real estate.

Real Estate Evaluation in Germany

The real estate market is at risk to produce economic bubbles, which produce heavy financial losses while bursting. To flatten these bubbles, Germany tried since the 1960s a way of thorough analysis and high transparency of the real estate market as a public task. The experts from different professions in the local market effectively are included in a honorary form. The competition between the experts and also between the experts and the public institution is intentionally maintained.

Research on Rural Collective Land Ownership Confirmation Based on Unified Registration of Real Estate

Rural collective land ownership registration is a basic work to safeguard farmers＇ land rights and interests, and promote rural reform and development, which involves the vital interests of the peasants, and has great influence on the rural economic and social development. However, there are many problems in rural col- lective land ownership registration and certification, such as unclear rural land own- ership subject and nature; not unified understanding of registration, no high enthusi- asm of the grass-roots work; prominent registration policy contradictions, serious shortage of investment, which seriously affect the rural land ownership registration work. In order to solve these problems, some suggestions are put forward on pro- moting the rural land ownership registration work smoothly.

Analysis of the Development of Real Estate Industry from the Perspective of Supply Side Structural Reform

The role of the real estate industry plays a very important in China＇s eco- nomic reform, and in recent years, with the onomy, the real estate enterprises in China progressive development of market ec- have become weak. However. the ＂hot real estate＂ seems to be in constantly upgrading. Industry development is necessary for the operation of each enterprise. Development is an important part of the strate- gy of an enterprise, and according to the current situation of development of real estate economy, it is very important for the development of the whole economy to predict the future development trend. The proposal of supply side structural reform measures could resolve the problems of remaining capacity and high inventory, which makes China＇s real estate industry present a new atmosphere. In this paper, a typical case, the listed real estate company ZHONG HUA Real Estate Enterprise Company Limited was selected to analyze its conditions, finding that inventory ex- cess production capacity and low land use efficiency were common problems in the development process of real estate industry at present. In order to solve these problems, some concrete suggestions and countermeasures were proposed from the government control management, real estate management and property buyers de- mand management.

Empirical evidence on the ownership and liquidity of real estate tokens

To better understand the potential and limitations of the tokenization of real asset mar-kets,empirical studies need to examine this radically new organization of financial mar-kets.In our study,we examine the financial and economic consequences of tokenizing 58 residential rental properties in the US,particularly those in Detroit.Tokenization aims at fragmented ownership.We found that the residential properties examined have 254 owners on average.Investors with a greater than USD 5,000 investment in real estate tokens,diversify their real estate ownership across properties within and across the cities.Property ownership changes about once yearly,with more changes for proper-ties on decentralized exchanges.We report that real estate token prices move accord-ing to the house price index;hence,investing in real estate tokens provides economic exposure to residential house prices.

On Financing Models of Real Estate Enterprise

Against the background that the central government is strengthening macro control and tightening credit scale of real estate businesses, financing capacity becomes the determinant for a real estate enterprise＇s development. Domestic enterprises have many ways of financing, and this paper describes ten common models, simply analyses the advantages and disadvantages of each model, while explains and recommends three financing models with practical examples： individual entrusted loan, trust financing and assets securitization.

Prioritizing real estate enterprises based on credit risk assessment:an integrated multi‑criteria group decision support framework

Credit risk assessment involves conducting a fair review and evaluation of an assessed subject’s solvency and creditworthiness.In the context of real estate enterprises,credit risk assessment provides a basis for banks and other financial institutions to choose suitable investment objects.Additionally,it encourages real estate enterprises to abide by market norms and provide reliable information for the standardized management of the real estate industry.However,Chinese real estate companies are hesitant to disclose their actual operating data due to privacy concerns,making subjective evalu-ation approaches inevitable,occupying important roles in accomplishing Chinese real estate enterprise credit risk assessment tasks.To improve the normative and reliability of credit risk assessment for Chinese real estate enterprises,this study proposes an integrated multi-criteria group decision-making approach.First,a credit risk assessment index for Chinese real estate enterprises is established.Then,the proposed framework combines proportional hesitant fuzzy linguistic term sets and preference ranking organization method for enrichment evaluation II methods.This approach is suitable for processing large amounts of data with high uncertainty,which is often the case in credit risk assessment tasks of Chinese real estate enterprises involving massive subjec-tive evaluation information.Finally,the proposed model is validated through a case study accompanied by sensitivity and comparative analyses to verify its rationality and feasibility.This study contributes to the research on credit assessment for Chinese real estate enterprises and provides a revised paradigm for real estate enterprise credit risk assessment.

Research on the intelligent supervision and operation platform of railway real estate

Railway real estate is the fundamental element of railway transportation production and operation.Effective management and rational utilization of railway real estate is essential for railway asset operation.Based on the investigation of the requirements of railway real estate management and operation,combined with Beidou positioning,GIS(Geographic Information System),multi-source data fusion and other cutting-edge technologies,this paper puts forward the multi-dimensional dynamic statistical method of real estate information,the identification method of railway land occupation and the comprehensive evaluation method of real estate development and utilization potential,and build the railway real estate supervision and operation platform,design the function of the platform,so as to provide intelligent solutions for the railway real estate operation.

Real Estate Management via a Decentralized Blockchain Platform

Blockchain technology is one of the key technological breakthroughs of the last decade.It has the ability to revolutionize numerous aspects of society,including financial systems,healthcare,e-government and many others.One such area that is able to reap the benefits of blockchain technology is the real estate industry.Like many other industries,real estate faces major administrative problems such as high transaction fees,a lack of transparency,fraud and the effects of a middleman including undue influence and commissions.Blockchain enables supporting technologies to overcome the obstacles inherent within the real estate investment market.These technologies include smart contracts,immutable record management and time-stamped storage.We utilize these key properties of blockchain technology in our work by proposing a system that has the ability to record real estate transactions in a private blockchain,using smart contracts.The immutability of the blockchain ledger and transactions can provide a safe space for the real estate business.Blockchain technology can also assist the authentication process by hastening background checks.Personal digital keys are provided to parties that are involved in a contract,thus minimizing the risk of fraud.We also discuss the rationale behind the advantages of using a blockchain in this manner,and how we selected the consensus mechanism for our proposed system.

The analysis of capital structure of Chinese real estate listed companies

Capital structure is the basis for assessing the efficiency of enterprise financing activity. The rational capital structure could reduce the cost of capital and plays an important role in managing a company. Thus this paper, based on the real estate listed companies on Shenzhen and Shanghai stock markets, analyzes the factors which influence capital structure, such as profitability, size, collateral value of assets, growth, stockholder’s equity, concentration degree of control power, ownership attribute, and applied statistics tools used to help the real estate listed companies optimize the structure of capital.

Process-oriented organizational structure and its application in real estate development enterprises in China

This paper analyzes the shortcomings of the current organizational structure of real estate development enterprises in China. Integrating the idea of Business Process Reengineering （BPR） with organizational reformation, the authors bring out a process-orlented organizational structure. A further study on the application of process-oriented organizational structure in real estate development enterprises is carried out in a test case. Using residence-development process as an example, the authors illustrate the composition and operation of the business process system of real estate development enterprises.

A group consensus model for evaluating real estate investment alternatives

Background:Individual decision-making largely influences the effectiveness of decisions and benefits of investments.Methods:In this article,a consensus model for group decision-making(GDM),based on the analytic hierarchy process(AHP),is developed to gather group ideas and analyze the real estate investment environment under multi-criteria problems.Twelve evaluation procedures of the developed model,which increase the convergence of the opinions of multiple experts,are proposed.Results:An empirical case about the real estate investment environment is applied to certify the feasibility of this developed model.Conclusions:the evaluation procedures have been fully observed with several rounds of discussions,and have manifested the experiences of experts.Besides,the evaluation results are in accordance with real-world situations,which demonstrates that our developed model is a feasible analysis tool for real estate investors to obtain better profits and lower risk.

Analysis on using UPREITs in real estate finance in United States

This paper gave an overview introduction about umbrella partnership real estate investment trust (UPREIT) to the readers who are not related to real estate and accounting major. To show how UPREIT defer capital gain taxes,makes good cash flow and maximize profit in real estate finance,the paper included the theory and structure of UPREIT(real estate investment trust),partnership issues,advantage and disadvantage,and created a proforma to demonstrate how UPREIT works.

Research on the Effect of Artificial Intelligence Real Estate Forecasting Using Multiple Regression Analysis and Artificial Neural Network: A Case Study of Ghana

To transition from conventional to intelligent real estate, the real estate industry must enhance its embrace of disruptive technology. Even though the real estate auction market has grown in importance in the financial, economic, and investment sectors, few artificial intelligence-based research has tried to predict the auction values of real estate in the past. According to the objectives of this research, artificial intelligence and statistical methods will be used to create forecasting models for real estate auction prices. A multiple regression model and an artificial neural network are used in conjunction with one another to build the forecasting models. For the empirical study, the study utilizes data from Ghana apartment auctions from 2016 to 2020 to anticipate auction prices and evaluate the forecasting accuracy of the various models available at the time. Compared to the conventional Multiple Regression Analysis, using artificial intelligence systems for real estate appraisal is becoming a more viable option (MRA). The Artificial Neural network model exhibits the most outstanding performance, and efficient zonal segmentation based on the auction evaluation price enhances the model’s prediction accuracy even more. There is a statistically significant difference between the two models when it comes to forecasting the values of real estate auctions.