Identifying Factors Affecting the Pricing of Housing Facility Bonds

Falah  Doostan Asl; Arash  Hadizadeh Miyarkolaee; Amir  Mohammadzadeh

doi:10.61838/

Authors

Falah Doostan Asl Department of Financial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran

Arash Hadizadeh Miyarkolaee * Department of Economics, Qazvin Branch, Islamic Azad University, Qazvin, Iran arash.hadizade@gmail.com

Amir Mohammadzadeh Department of Financial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran

https://doi.org/10.61838/

Keywords:

Housing facility bonds, bond pricing, financial indices, market conditions, Delphi technique, Housing Bank

Abstract

Objective: The objective of this study is to identify the financial and non-financial factors influencing the pricing of housing facility bonds at the Housing Bank of Iran.

Methodology: The study utilizes a qualitative research approach, employing the Delphi technique in two rounds. Seven experts and specialists from the Housing Bank were selected based on their expertise in bond pricing. Data were collected through note-taking and semi-structured interviews. The analysis was conducted by coding and categorizing the extracted concepts into four primary indices. Kendall’s coefficient of concordance was used to measure consensus among experts.

Findings: The analysis identified 35 key components grouped into four indices—fluctuations, restrictions, market, and technical factors. The main drivers of bond pricing fluctuations include increased demand for construction loans without deposits, consumer demand for home purchases using loan bonds, and a rise in real estate investors seeking housing facility bonds. Factors such as interest rate fluctuations, housing market expectations, and seasonal effects were also found to significantly impact bond prices.

Conclusion: The study concludes that fluctuations, restrictions, market conditions, and technical factors are critical in determining housing facility bond prices for corporate clients at the Housing Bank. It is recommended that the bank adopt a combined method of artificial neural networks, genetic algorithms, and logistic regression to forecast bond pricing based on these indices. Additionally, developing financial instruments based on housing facility bonds can improve pricing accuracy in the capital market.