IDEAS home Printed from https://ideas.repec.org/a/spr/jcomop/v45y2023i2d10.1007_s10878-023-00995-x.html
   My bibliography  Save this article

An equilibrium analysis of the impact of real estate price volatility on macroeconomics based on ant colony algorithm

Author

Listed:
  • Zhou Jie

    (Northwestern Polytechnical University)

  • Chai Hua Qi

    (Northwestern Polytechnical University)

Abstract

In view of the large subjective arbitrariness of traditional real estate appraisal methods, through analysis of the advantages and disadvantages of the least square support vector machine (LS-SVM) model for real estate appraisal, the use of ant colony algorithm is proposed for the parameter selection problem (ACO) is optimized, and a real estate valuation model based on ant colony algorithm optimization least square support vector machine (ACO-LS-SVM) is established after integration. On this basis, in order to study the impact of real estate price fluctuations on the macroeconomic equilibrium, this paper studies the impact of real estate price fluctuations on my country’s macroeconomic and sectoral economies by constructing a computable general equilibrium model. The main findings are as follows: the output of various industries and the direction of changes in real estate prices are consistent, but industries such as heavy industry, extractive industries, and real estate are relatively affected; the rise and fall of real estate prices have caused the income of all residents to decline, but each income The impacts on urban residents of different ranks are not the same, and the incomes of the government and enterprises are in the same direction as the changes in real estate prices. The efficiency of the predicted price of real estate appraisal technique is 4.08% and the average relative error is 1.78%. On the whole, the increase in real estate prices has a greater driving effect on economic growth, but the negative impact of a price drop of the same magnitude is greater.

Suggested Citation

  • Zhou Jie & Chai Hua Qi, 2023. "An equilibrium analysis of the impact of real estate price volatility on macroeconomics based on ant colony algorithm," Journal of Combinatorial Optimization, Springer, vol. 45(2), pages 1-26, March.
    • Handle: RePEc:spr:jcomop:v:45:y:2023:i:2:d:10.1007_s10878-023-00995-x
    DOI: 10.1007/s10878-023-00995-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10878-023-00995-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10878-023-00995-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Bórawski, Piotr & Bełdycka-Bórawska, Aneta & Szymańska, Elżbieta Jadwiga & Jankowski, Krzysztof Józef & Dunn, James W., 2019. "Price volatility of agricultural land in Poland in the context of the European Union," Land Use Policy, Elsevier, vol. 82(C), pages 486-496.
    2. John Elder, 2019. "Oil price volatility and real options: 35 years of evidence," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(12), pages 1549-1564, December.
    3. Yuichiro Kawaguchi & Jarjisu Sa-Aadu & James D. Shilling, 2017. "REIT Stock Price Volatility and the Effects of Leverage," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 45(2), pages 452-477, April.
    4. Ma, Feng & Liu, Jing & Wahab, M.I.M. & Zhang, Yaojie, 2018. "Forecasting the aggregate oil price volatility in a data-rich environment," Economic Modelling, Elsevier, vol. 72(C), pages 320-332.
    5. Abdollahi, Hooman, 2020. "A novel hybrid model for forecasting crude oil price based on time series decomposition," Applied Energy, Elsevier, vol. 267(C).
    6. Christos S. Savva & Nektarios A. Michail, 2017. "Modelling house price volatility states in Cyprus with switching ARCH models," Cyprus Economic Policy Review, University of Cyprus, Economics Research Centre, vol. 11(1), pages 69-82, June.
    7. Deng, Yongheng & Girardin, Eric & Joyeux, Roselyne, 2018. "Fundamentals and the volatility of real estate prices in China: A sequential modelling strategy," China Economic Review, Elsevier, vol. 48(C), pages 205-222.
    8. Haiwei Chen, 2017. "Real Estate Transfer Taxes and Housing Price Volatility in the United States," International Real Estate Review, Global Social Science Institute, vol. 20(2), pages 207-219.
    9. Luo, Xingguo & Qin, Shihua, 2017. "Oil price uncertainty and Chinese stock returns: New evidence from the oil volatility index," Finance Research Letters, Elsevier, vol. 20(C), pages 29-34.
    10. Chen, Jiawei & Hui, Eddie Chi-Man & Seiler, Michael J. & Zhang, Hong, 2018. "Household tenure choice and housing price volatility under a binding home-purchase limit policy constraint," Journal of Housing Economics, Elsevier, vol. 41(C), pages 124-134.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hao, Jun & Feng, Qianqian & Yuan, Jiaxin & Sun, Xiaolei & Li, Jianping, 2022. "A dynamic ensemble learning with multi-objective optimization for oil prices prediction," Resources Policy, Elsevier, vol. 79(C).
    2. Lyócsa, Štefan & Todorova, Neda & Výrost, Tomáš, 2021. "Predicting risk in energy markets: Low-frequency data still matter," Applied Energy, Elsevier, vol. 282(PA).
    3. Yu Wei & Lan Bai & Kun Yang & Guiwu Wei, 2021. "Are industry‐level indicators more helpful to forecast industrial stock volatility? Evidence from Chinese manufacturing purchasing managers index," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(1), pages 17-39, January.
    4. Jin Boon Wong & Qin Zhang, 2020. "Impact of international energy prices on China's industries," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(5), pages 722-748, May.
    5. Liu, Jing & Ma, Feng & Zhang, Yaojie, 2019. "Forecasting the Chinese stock volatility across global stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 466-477.
    6. Wen, Danyan & Liu, Li & Wang, Yudong & Zhang, Yaojie, 2022. "Forecasting crude oil market returns: Enhanced moving average technical indicators," Resources Policy, Elsevier, vol. 76(C).
    7. Chen, Chun-Da & Cheng, Chiao-Ming & Demirer, Rıza, 2017. "Oil and stock market momentum," Energy Economics, Elsevier, vol. 68(C), pages 151-159.
    8. Lu, Fei & Ma, Feng & Li, Pan & Huang, Dengshi, 2022. "Natural gas volatility predictability in a data-rich world," International Review of Financial Analysis, Elsevier, vol. 83(C).
    9. Woo, Chiew Eng & Kun, Sek Siok, 2019. "Examining Asymmetric Oil Price Exposure to Assets Return in Malaysia: A Nonlinear ARDL Approach," Jurnal Ekonomi Malaysia, Faculty of Economics and Business, Universiti Kebangsaan Malaysia, vol. 53(3), pages 23-41.
    10. Benjamin Kwakye & Chan Tze Haw, 2020. "Interplay of the Macroeconomy and Real Estate: Systematic Review of Literature," International Journal of Economics and Financial Issues, Econjournals, vol. 10(5), pages 262-271.
    11. Vera Ivanyuk, 2021. "Formulating the Concept of an Investment Strategy Adaptable to Changes in the Market Situation," Economies, MDPI, vol. 9(3), pages 1-19, June.
    12. Vladimir Pyrlik & Pavel Elizarov & Aleksandra Leonova, 2021. "Forecasting Realized Volatility Using Machine Learning and Mixed-Frequency Data (the Case of the Russian Stock Market)," CERGE-EI Working Papers wp713, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    13. Tissaoui, Kais & Azibi, Jamel, 2019. "International implied volatility risk indexes and Saudi stock return-volatility predictabilities," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 65-84.
    14. Lan Bai & Xiafei Li & Yu Wei & Guiwu Wei, 2022. "Does crude oil futures price really help to predict spot oil price? New evidence from density forecasting," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 3694-3712, July.
    15. Jiqian Wang & Rangan Gupta & Oğuzhan Çepni & Feng Ma, 2023. "Forecasting international REITs volatility: the role of oil-price uncertainty," The European Journal of Finance, Taylor & Francis Journals, vol. 29(14), pages 1579-1597, September.
    16. Jiang, Ping & Liu, Zhenkun & Wang, Jianzhou & Zhang, Lifang, 2021. "Decomposition-selection-ensemble forecasting system for energy futures price forecasting based on multi-objective version of chaos game optimization algorithm," Resources Policy, Elsevier, vol. 73(C).
    17. Seyedeh Fatemeh Razmi & Bahareh Ramezanian Bajgiran & Seyed Mohammad Javad Razmi & Kiana Baensaf Oroumieh, 2020. "The Effects of External Uncertainties against Monetary Policy Uncertainty on IRANIAN Stock Return Volatility Using GARCH-MIDAS Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 10(4), pages 278-281.
    18. Mushtaq Hussain Khan & Junaid Ahmed & Mazhar Mughal & Imtiaz Hussain Khan, 2023. "Oil price volatility and stock returns: Evidence from three oil‐price wars," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(3), pages 3162-3182, July.
    19. Mishra, Shekhar & Mishra, Sibanjan, 2021. "Are Indian sectoral indices oil shock prone? An empirical evaluation," Resources Policy, Elsevier, vol. 70(C).
    20. Li, Xinyu & Wu, Meng & Yuan, Luqi & Xiao, Meng & Zhong, Ronghao & Yu, Miao, 2024. "Uncertainties and oil price volatility: Can lasso help?," Finance Research Letters, Elsevier, vol. 61(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jcomop:v:45:y:2023:i:2:d:10.1007_s10878-023-00995-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.