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 compar...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.展开更多
This paper raises the comparison method of operational stages of the real estate market. In order to select similar operational stages, we established an analysis model by applying fuzzy grade-of-membership clustering...This paper raises the comparison method of operational stages of the real estate market. In order to select similar operational stages, we established an analysis model by applying fuzzy grade-of-membership clustering in this paper. Firstly, we select the materials information of the real estate market in America, Germany and Japan. Secondly, the real estate markets of America, Germany and Japan are divided into several different stages. Lastly, we apply the method of fuzzy grade-of-membership clustering to select comparable stages. The result of analysis indicates that the real estate market of Japan and Germany (1960-1980) are similar to the market in China.展开更多
This paper first introduces the overview of real estate appraisal theory,the basic situation of real estate appraisal,the basic principles and characteristics of real estate appraisal,and three main appraisal methods....This paper first introduces the overview of real estate appraisal theory,the basic situation of real estate appraisal,the basic principles and characteristics of real estate appraisal,and three main appraisal methods.On this basis,the specific use scope of each method,the meaning of each parameter,and the influencing factors are analyzed.Then,based on the in-depth study of various existing appraisal theories and methods,a new appraisal method is proposed:the appraisal analysis method based on fuzzy analytic hierarchy process.展开更多
On the basis of determining the international comparison method and framework of the real estate market, this paper makes a comparative analysis on the development of China’s real estate market and those of some comp...On the basis of determining the international comparison method and framework of the real estate market, this paper makes a comparative analysis on the development of China’s real estate market and those of some comparable countries (or regions). The prominent problem of the real estate market in our country is the rapid rise of urban house price and the high ratio of house price to income. The direct reason is that the social funds, especially the excessive credit funds flow into the real estate market. The indirect reason is that the land supply mode makes the land price increase too fast and account for high proportion in house price, which leads to the result that the development of real estate industry is beyond the stage of economic development. These problems are not isolated, but related to each other. The rapid rise of house prices in most of the cities, especially in the first-tier cities, is the comprehensive reflection of a large amount of credit funds promoting and the drawbacks of the land supply system in essence. The main ideas to solve the problem are correctly understanding the status, role and influence of the real estate market, stabilizing and improving the short-term control policy of the real estate market, and accelerating the establishment of a long-term mechanism for the healthy development of the real estate market.展开更多
文摘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.
文摘This paper raises the comparison method of operational stages of the real estate market. In order to select similar operational stages, we established an analysis model by applying fuzzy grade-of-membership clustering in this paper. Firstly, we select the materials information of the real estate market in America, Germany and Japan. Secondly, the real estate markets of America, Germany and Japan are divided into several different stages. Lastly, we apply the method of fuzzy grade-of-membership clustering to select comparable stages. The result of analysis indicates that the real estate market of Japan and Germany (1960-1980) are similar to the market in China.
文摘This paper first introduces the overview of real estate appraisal theory,the basic situation of real estate appraisal,the basic principles and characteristics of real estate appraisal,and three main appraisal methods.On this basis,the specific use scope of each method,the meaning of each parameter,and the influencing factors are analyzed.Then,based on the in-depth study of various existing appraisal theories and methods,a new appraisal method is proposed:the appraisal analysis method based on fuzzy analytic hierarchy process.
基金Major projects of the National Social Science Fund“Research on China’s Industrial Structure Adjustment and Upgrading under the Supply-Side Reform and Coordinated Demand-Side Reform during the 13th Five-Year Plan Period”(16ZDA004)。
文摘On the basis of determining the international comparison method and framework of the real estate market, this paper makes a comparative analysis on the development of China’s real estate market and those of some comparable countries (or regions). The prominent problem of the real estate market in our country is the rapid rise of urban house price and the high ratio of house price to income. The direct reason is that the social funds, especially the excessive credit funds flow into the real estate market. The indirect reason is that the land supply mode makes the land price increase too fast and account for high proportion in house price, which leads to the result that the development of real estate industry is beyond the stage of economic development. These problems are not isolated, but related to each other. The rapid rise of house prices in most of the cities, especially in the first-tier cities, is the comprehensive reflection of a large amount of credit funds promoting and the drawbacks of the land supply system in essence. The main ideas to solve the problem are correctly understanding the status, role and influence of the real estate market, stabilizing and improving the short-term control policy of the real estate market, and accelerating the establishment of a long-term mechanism for the healthy development of the real estate market.
