Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1
Lida Wang
 -- 1910 2022-06-22 17:59:12 |
2 format correct
Catherine Yang
 Meta information modification 1910 2022-06-24 08:07:22 |

Video Upload Options

We provide professional Video Production Services to translate complex research into visually appealing presentations. Would you like to try it?
No, upload directly Yes

Confirm

Are you sure to Delete?
Yes No
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Wang, L.;  Rong, X.;  Chen, Z.;  Mu, L.;  Jiang, S.; Wang, L. A Real Estate Early Warning System. Encyclopedia. Available online: https://encyclopedia.pub/entry/24360 (accessed on 27 December 2024).
Wang L,  Rong X,  Chen Z,  Mu L,  Jiang S, Wang L. A Real Estate Early Warning System. Encyclopedia. Available at: https://encyclopedia.pub/entry/24360. Accessed December 27, 2024.
Wang, Lida, Xian Rong, Zeyu Chen, Lingling Mu, Shan Jiang, Lida Wang. "A Real Estate Early Warning System" Encyclopedia, https://encyclopedia.pub/entry/24360 (accessed December 27, 2024).
Wang, L.,  Rong, X.,  Chen, Z.,  Mu, L.,  Jiang, S., & Wang, L. (2022, June 22). A Real Estate Early Warning System. In Encyclopedia. https://encyclopedia.pub/entry/24360
Wang, Lida, et al. "A Real Estate Early Warning System." Encyclopedia. Web. 22 June, 2022.
A Real Estate Early Warning System
Edit

The real estate market is vital for national economic development, and it is of great significance to research an early warning method to identify an abnormal status of the real estate market. There is the warning status of the Beijing real estate market went from a fluctuation status to a stable “Normal” status from 2000 to 2020, and the warning status is expected to be more stable under a “Normal” status in the next decade under the same political and economic environment. The PSO-LSSVR model was found to have accurate prediction ability and demonstrated generalization ability. 

real estate market early warning system PSO-LSSVR algorithm sustainable development

1. Introduction

Over the past few decades, the rapid development of China’s real estate market has actively contributed to the rapid growth of China’s GDP, especially after the 2008 global economic crisis. Moreover, the rapid development of the real estate industry has also led to the development of the construction industry, decoration, home appliances, furniture industry and so on. According to the China Statistical Yearbook 2021, the GDP of the real estate industry accounted for 7.3 percent of the country’s total GDP, and the property-related construction industry accounted for 7.2 percent of the country’s total GDP in the year 2020 [1]. On the other hand, the real estate industry has become an important part of fixed asset investments, where fixed asset investments are one of the troikas for economic growth (investment, export and consumption). Hence, the developing real estate industry has become one of the important economic pillars of China’s economic development. However, the excessive development of any industry will bring negative effects on society.
First, when the real estate boom is excessively prosperous, there will be a large number of excess funds, bank loans and corporate funds flowing into the real estate market, which will lead to the phenomenon of “industry emptiness” (the decline in the development level of the primary and secondary industry lead to the unbalanced structural proportion of national economic demand and the continuous shrinkage of domestic investment, which, in turn, lead to the substantial reduction in employment opportunities and social problems, such as a rising unemployment rate) [2].
Second, the development of the real estate market is closely related to the macroeconomy. Excessive development of the real estate market promotes the investment flow into related industries, which leads to a shortage of resources supplied to other industries [3]. To solve such conditions, the government usually restrains the development of the real estate industry through credit regulation and land regulation policies, which leads to the rapid cooling of the real estate industry and economic recession [4]. The excessive development of the real estate industry and the drastic macro-control will lead to excessive fluctuations in the macroeconomy [5].
Last, but not least, the rapid development of the real estate industry has introduced a large amount of investment of capital and the public. Excessive investment will lead to the market price being greater than the actual price, which leads to economic bubbles in the real estate industry [6]. When the bubble bursts, the loans from financial institutions and banks to real estate enterprises will turn into bad debts, which will lead to the occurrence of an economic crisis and financial crisis in serious cases, and then lead to industrial depression, the sharp increase in unemployment and crime rate, and other social problems [7]. The economic depression caused by the bursting of the real estate bubble will bring enormous harm to the national development and living quality, as the U.S. real estate bubble burst in the late 1980s led to the bank crisis, and at the beginning of the 21st century, the U.S. real estate bubble burst led to the subprime mortgage crisis and the biggest financial crisis since 1930. The real estate bubble burst in Japan in the 1980s set off a decades-long economic recession. These classic real estate bubble bursts offer warnings for other countries’ real estate developments.
In contrast, if the real estate industry appears excessively depressed, the public, capital and government will lose confidence in the real estate industry, which will further affect the development of the real estate industry and the development of the macroeconomy [8]. Moreover, the construction industry, which is closely related to the real estate industry, and the back-end industries will be seriously affected [9]. Therefore, it is necessary to establish an early warning system for the real estate market to prevent the bubble and further enormous harm to the national and social development. A real estate market early warning system can also help the urban government and supervisory agencies to reduce the risk of bubbles in advance [10]. Furthermore, a real estate market early warning system can provide a scientific basis for the government’s macro-control and correctly guide investors away from irrational investment and consumption in the real estate market to promote the healthy and stable development of the market [11].

2. History and Development

The real estate industry has the characteristics of information asymmetry, fixedness and transaction dispersion [12][13], and the effective balance of the general industrial structure and supply–demand relationship of the real estate industry cannot be achieved only through market forces [14]. Hence, research on the real estate market is complicated and diverse. With the continuous development of science and technology, more and more automatic methods were applied to the sustainable development of the real estate market, such as artificial intelligence [15] and machine learning [16]. 
In a previous study, Pyhrr et al. observed the cyclical phenomenon of the Western real estate market since the 1960s and then researched the change laws and periodic mechanism of the real estate market [17][18]. With the in-depth study of the real estate cycle, scholars gradually realized the importance of early warning for the real estate market. In the 1990s, Nieboer et al. first established a real estate market early warning system based on the housing vacancy rate by studying the relationship between the housing vacancy rate and the trend of the real estate market in the Netherlands [19]. Subsequently, as a country with a well-developed real estate market, the United States found the real estate downturn signal of the Chicago community by observing the fluctuation and mechanism of the real estate cycle, and then detecting seven abnormal change indicators and establishing the original real estate early warning model [20]. With the proposal of the Case–Shiller (CS) housing price index, scholars used to research the relationship between the real estate market and macroeconomic phenomena by combining the Case–Shiller housing price index and early warning leading indicators [21][22]. Huang et al. adopted the deep learning method in the Time-Varying Parameter-Vector Autoregression (TVP-VAR) model to research the monitoring and early warning of systemic financial risk, where the results showed that the real estate market is closely related to the country’s systemic financial risk [23].
In addition, scholars in different fields researched the methods and applications of early warning systems [24]. With the development of the research field, the concept and methods of early warning systems have been widely introduced into the aerospace study [25][26], ecological systems research [27], seismic research [28][29], transportation study [30][31], medical study [32][33] and real estate study [34].
With the rapid development of science and technology, more methods have been adopted in the research of early warning systems. Begusic et al. adopted the system dynamics to research the system risk in the financial market and real estate market, which was achieved by researching the spatio-temporal spillover effect of system information feedback to obtain early warning signals of the market [35]. Huang and Feng proposed an early warning method based on the monitor indicators and statistical method and simulated the policy regulation of the real estate market in Shenzhen by combining system dynamics [36]. Yang used computer technology based on the principle of the PROBIT model to design an early warning system to analyze risk verification in the real estate market and the results showed that the early warning system had an approximately 85% early warning rate [37]. Kholodilin and Michelsen used modern machine learning methods to forecast the real estate market bubbles in Germany and presented an early warning model to analyze the speculative risk in the real estate market; furthermore, they provided some market intervention suggestions for the German government [38]. Wang et al. analyzed the real estate market in Beijing by establishing an early warning model based on multi-class support vector machines and put forward policy suggestions for the healthy operation of the real estate market [34]. Based on previous research on the real estate market, it was found that the machine learning model has better processing capacity than the transmission statistical model for solving nonlinear problems [39][40]. In recent years, scholars established early warning index systems for the real estate market through different methods, and conducted early warning research through qualitative and quantitative methods, promoting the research of early warning index systems to gradually reach a mature stage of development.
Based on the relatively mature development of the real estate early warning indicators research, further research of indicator selection should take into account the market supply–demand relationship, internal coordination, external economic factors and so on. The methods for early warning systems were developed from basic index analysis research methods to statistical research methods and then to comprehensive early warning models. Moreover, with the in-depth research, the early warning research of the real estate market has changed from qualitative research to a combination of qualitative and quantitative research. With the rapid development of computer science and artificial intelligence, more and more theories and methods were introduced into the real estate early warning model, and new research ideas were developed [41]. However, in the current research on the real estate market early warning, the problems of a small sample, overall generalization ability and nonlinearity cannot be solved easily.
Due to the complexity of the real estate market system and the lack of historical data, there will be insufficient training, unstable performance and overlearning when the neural network is training. All of these factors will lead to unsatisfactory prediction results. As a result, Cortes [42] proposed the support vector regression (SVM), which has demonstrated excellent performance in solving small-sample, nonlinear, high-dimensional problems. That is why SVR, the regression version of SVM, is widely used in energy research [43]. SuyKens et al. [44] improved least-squares support vector regression (LSSVR) in 1999, the least-squares formulation of SVR, which has better generalization abilities and powerful computation. Chou et al. [45] found that LSSVR is more accurate than regression analysis and neural networks for building energy consumption. However, there are still disadvantages to LSSVR due to the choice of kernel and regularization parameters; therefore, particle swarm optimization (PSO) was introduced to improve the LSSVR. PSO-LSSVR was successfully employed in many fields, especially in the evaluation of engineering projects. For this reason, some scholars specifically analyzed and examined its operating principle [46][47]. In addition, the LSSVR model has the characteristics of a fast training speed, which is helpful to obtain a more effective regression model in a complex system. The PSO model has the characteristic of a fast convergence speed, which can further improve the accuracy of the prediction. Through continuous iteration of the PSO model, the most appropriate prediction parameters can be quickly found. The most important point is that the LSSVR model is suitable for exploring potential lows from historical data and predictions, while the PSO model has relatively low requirements for optimization functions; therefore, the PSO-LSSVR model is more suitable for the research of the real estate market with small data [48]. However, PSO-LSSVR in real estate market prediction has not been widely applied.

This entry is adapted from 10.3390/buildings12060706

References

  1. Statistics Bureau of the People’s Republic of China, China Statistical Yearbook. 2021. Available online: http://www.stats.gov.cn/ (accessed on 18 March 2022). (In Chinese)
  2. Yan, J.Z.L. Empirical Researches on Macroeconomic Influence Factors in Real Estate Based on Data Mining (DM). Agro Food Ind. Hi-Tech 2017, 28, 2729–2732.
  3. Kuil, P.; Wellner, K. The Imperatives of the Real Estate Industry and its Effects on Architecture and the Built Environment; Case Studies Presentation. In Proceedings of the 24th Annual European Real Estate Society Conference, Delft, The Netherlands, 30 June 2017.
  4. Carrasco-Gallego, J.A. Real Estate, Economic Stability and the New Macro-Financial Policies. Sustainability 2020, 13, 236.
  5. Jian, C.; Cao, P.; Zhou, X.; Lai, K.K.; Chen, X.; Su, S. The Conductive and Predictive Effect of Oil Price Fluctuations on China's Industry Development Based on Mixed-Frequency Data. Energies 2018, 11, 1372.
  6. Brzezicka, J. Towards a typology of housing price bubbles: A literature review. Hous. Theory Soc. 2021, 38, 320–342.
  7. Machaj, M. Can the Taylor Rule be a Good Guidance for Policy? The Case of 2001–2008 Real Estate Bubble. Prague Econ. Pap. 2016, 25, 381–395.
  8. Liow, K.H.; Zhou, X.; Ye, Q. Correlation Dynamics and Determinants in International Securitized Real Estate Markets. Real Estate Econ. 2015, 43, 537–585.
  9. Yan, X.; Lin, X. Business Expanding and Strategic Decision-Making Innovation for Real Estate Valuation Companies Based on Big-Data. In Proceedings of the 20th International Symposium on Advancement of Construction Management and Real Estate; Springer: Singapore, 2017; pp. 1057–1068.
  10. Gu, H. Macroeconomic early warning research: Theory, method, history. Econ. Theor. Econ. Manag. 1997, 4, 1–7.
  11. Xiao, L. Research on Urban Residential Area Early Warning System Based on Artificial Neural Network. Agro Food Ind. HI-Tech 2017, 28, 2250–2254.
  12. Vergara-Perucich, J.; Aguirre-Nunez, C. Housing Prices in Unregulated Markets: Study on Verticalised Dwellings in Santiago de Chile. Buildings 2020, 10, 6.
  13. Echeverri, N.; Jylha, T.; Koppels, P. Searching for Flexibility in Corporate Real Estate Portfolio: Six Co-Working Strategies for User Corporations. Buildings 2021, 11, 115.
  14. Zhao, X.; Gao, Y. Impact of Transaction Characteristics on the Performance of Mergers and Acquisitions (M and A) in Chinese Real Estate Industry. J. Tianjin Univ. (Soc. Sci.) 2017, 19, 119–124.
  15. Kang, J.; Lee, H.; Jeong, S.; Lee, H.; Oh, K. Developing a forecasting model for real estate auction prices using artificial intelligence. Sustainability 2020, 12, 2899.
  16. Yu, Y.; Lu, J.; Shen, D.; Chen, B. Research on real estate pricing methods based on data mining and machine learning. Neural Comput. Appl. 2021, 33, 3925–3937.
  17. Pyhrr, S.A.; Born, W.L.; Robinson, R.; Lucas, S.R. Real property valuation in a changing economic and market cycle. Apprais. J. 1996, 64, 14.
  18. Pyhrr, S.A. A Computer Simulation Model to Measure the Risk in Real Estate Investment. Real Estate Economics. 2010, 1, 48–78.
  19. Nieboer, N.; Voogd, H. Housing vacancy and early warning systems. Neth. J. Hous. Environ. Res. 1990, 5, 237–249.
  20. Hoffman, A.V.; Belsky, E.S.; Lee, K. The Impact of Housing on Community: A Review of Scholarly Theories and Empirical Research, Joint Center for Housing Studies, Graduate School of Design and John F. Kennedy School of Government; Harvard University: Cambridge, MA, USA, 2006.
  21. Chan, K.F.; Treepongkaruna, S.; Brooks, R.; Gray, S. Asset market linkages: Evidence from financial, commodity and real estate assets. J. Bank. Financ. 2011, 35, 1415–1426.
  22. Gonzalez, M.R.; Basse, T.; Kunze, F.; Vornholz, G. Early warning indicator systems for real estate investments: Empirical evidence and some thoughts from the perspective of financial risk management. Z. Für Gesamte Versicher. 2018, 107, 387–403.
  23. Huang, A.; Qiu, L.; Li, Z. Applying Deep Learning Method in TVP-VAR Model under Systematic Financial Risk Monitoring and Early Warning. J. Comput. Appl. Math. 2021, 382, 113065.
  24. Loris, A.B. A Financial Early Warning System for Over-the-Counter Broker-Dealers. J. Financ. 1976, 31, 1201–1217.
  25. Caruzzo, A.; Belderrain, M.C.N.; Fisch, G.; Young, G.S.; Hanlon, C.J.; Verlinde, J. Modelling weather risk preferences with multi-criteria decision analysis for an aerospace vehicle launch. Meteorol. Appl. 2018, 25, 456–465.
  26. Burnaev, E.V. On Construction of Early Warning Systems for Predictive Maintenance in Aerospace Industry. J. Commun. Technol. Electron. 2019, 64, 1473–1484.
  27. Clements, C.F.; Mccarthy, M.A.; Blanchard, J.L. Early warning signals of recovery in complex systems. Nat. Commun. 2019, 10, 1681.
  28. Kong, Q.; Allen, R.M.; Schreier, L.; Kwon, Y.-W. MyShake: A smartphone seismic network for earthquake early warning and beyond. Sci. Adv. 2016, 2, e1501055.
  29. Li, Z.; Meier, M.-A.; Hauksson, E.; Zhan, Z.; Andrews, J. Machine Learning Seismic Wave Discrimination: Application to Earthquake Early Warning. Geophys. Res. Lett. 2018, 45, 4773–4779.
  30. Wang, J.; Wang, J. A New Early Warning Method of Train Tracking Interval Based on CTC. IEEE Trans. Intell. Transp. Syst. 2017, 99, 1–7.
  31. Wang, H.; Li, L.; Pan, P.; Wang, Y.; Jin, Y. Early warning of burst passenger flow in public transportation system. Transp. Res. Part C Emerg. Technol. 2019, 105, 580–598.
  32. Kalliomäki, J.; Kontula, T.; Kalliomäki, M.L.; Kivipuro, M.; Tirkkonen, J.; Solin, J.; Pauniaho, S.-L.; Huhtala, H.; Yli-Hankala, A.; Hoppu, S. The national early warning score (NEWS) could not predict secondary transportation to the tertiary hospital in Finnish prehospital care. Resuscitation 2016, 106, e69.
  33. Kovacs, C. Outreach and early warning systems for the prevention of intensive care admission and death of critically ill adult patients on general hospital wards. Int. J. Nurs. Pract. 2016, 22, 523–525.
  34. Wang, X.-J.; Zeng, G.-T.; Zhang, K.-X.; Chu, H.-B.; Chen, Z.-S. Urban real estate market early warning based on support vector machine: A case study of Beijing. Int. J. Comput. Intell. Syst. 2020, 13, 153–166.
  35. Begusic, S.; Kostanjcar, Z.; Kovac, D.; Stanley, H.; Podobnik, B. Information feedback in temporal networks as a predictor of market crashes. Complexity 2018, 2018, 2834680.
  36. Huang, F.; Feng, W. A system for early-warning and forecasting of real estate development. Autom. Constr. 2005, 14, 333–342.
  37. Yang, J. Design of early warning multimedia computer system for real estate market under PROBIT model. Multimed. Tools Appl. 2019, 1–15.
  38. Kholodilin, K.A.; Michelsen, C. High Risk of a Housing Bubble in Germany and Most OECD Countries. DIW Wkly. Rep. 2019, 9, 265–273.
  39. Kim, T.Y.; Oh, K.J.; Sohn, I.; Hwang, C. Usefulness of Artificial Networks for Early Warning System of Economic Crisis. Expert Syst. Appl. 2004, 6, 583–590.
  40. Zheng, Y.J.; Chen, S.Y.; Xue, Y. A Pythagorean-Type Fuzzy Deep Denoising Autoencoder for Industrial Accident Early Warning. IEEE Trans. Fuzzy Syst. 2017, 25, 1561–1575.
  41. Park, D.; Ryu, D. A Machine Learning-Based Early Warning System for the Housing and Stock Markets. IEEE Access. 2021, 9, 3077962.
  42. Cortes, C.; Vapnik, V. Support-vector network. Mach. Learn. 1995, 20, 273–295.
  43. Izadyar, N.; Ghadamian, H.; Chong, W.T.; Moghadam, Z.; Tong, C.W.; Shamshirband, S. Appraisal of the support vector machine to forecast residential heating demand for the District Heating System based on the monthly overall natural gas consumption. Energy 2015, 93, 1558–1567.
  44. Suykens, J.; Vandewalle, J. Least Squares Support Vector Machine Classifiers. Neural Process. Lett. 1999, 9, 293–300.
  45. Chou, J.; Ngo, N. Time series analytics using sliding window metaheuristic optimization-based machine learning system for identifying building energy consumption patterns. Appl. Energy 2016, 177, 751–770.
  46. Xue, X.; Xiao, M. Deformation evaluation on surrounding rocks of underground caverns based on PSO-LSSVM. Tunn. Undergr. Space Technol. 2017, 69, 171–181.
  47. Chen, Y.; Zhou, Z.; Chen, Q. The research and application of LS-SVM based on particle swarm optimization. In Proceedings of the IEEE International Conference on Automation and Logistics, Jinan, China, 18–21 August 2007; pp. 1115–1120.
  48. Wang, B.; Tang, W.; Song, L.; Bai, G. PSO-LSSVR: A surrogate modeling approach for probabilitic flutter evaluation of compressor blade. Structures 2020, 28, 1634–1645.
More
©Text is available under the terms and conditions of the Creative Commons-Attribution ShareAlike (CC BY-SA) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
Upload a video for this entry
Information
Subjects: Operations Research & Management Science
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Lida Wang
,
Xian Rong
,
Zeyu Chen
,
Lingling Mu
,
Shan Jiang
,
Lida Wang
View Times: 697
Revisions: 2 times (View History)
Update Date: 24 Jun 2022
Table of Contents
    1000/1000
    Hot Most Recent
    Video Production Service
    Feedback