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Staff Working Papers Working Paper 24-03: The Lock-In Effect of Rising Mortgage Rates

​​​​​​Ross M. Batzer, Jonah R. Coste, William M. Doerner, and Michael J. Seiler

​​Ab​stract:

People can be “locked-in” or constrained in their ability to make appropriate financial changes, such as being unable to move homes, change jobs, sell stocks, rebalance portfolios, shift financial accounts, adjust insurance policies, transfer investment profits, or inherit wealth. These frictions—whether institutional, legislative, personal, or market-driven—are often overlooked. Residential real estate exemplifies this challenge with its physical immobility, high transaction costs, and concentrated wealth. In the United States, nearly all 50 million active mortgages have fixed rates, and most have interest rates far below prevailing market rates, creating a disincentive to sell. This paper finds that for every percentage point that market mortgage rates exceed the origination interest rate, the probability of sale is decreased by 18.1%. This mortgage rate lock-in led to a 57% reduction in home sales with fixed-rate mortgages in 2023Q4 and prevented 1.33 million sales between 2022Q2 and 2023Q4. The supply reduction increased home prices by 5.7%, outweighing the direct impact of elevated rates, which decreased prices by 3.3%. These findings underscore how mortgage rate lock-in restricts mobility, results in people not living in homes they would prefer, inflates prices, and worsens affordability. Certain borrower groups with lower wealth accumulation are less able to strategically time their sales, worsening inequality.​

​Mortgage lock-in data are available below in two formats at the bottom of this webpage. The first file offers a data supplement that could be used to recreate figures shown in the working paper. The second file offers additional developmental data aggregates produced from estimations in the working paper. Both files are subject to change with working paper revisions. Our  FA​Qs  address common questions about the datasets. Please cite this working paper when using either dataset.​

• Supplemental data​​ for figures
• ​​​ Developmental data aggregates ​

A  blog  has been written about the working paper. 

A REVIEW OF LITERATURE ON THE DETERMINANTS OF THE DEMAND FOR HOME LOANS

• Dr. Archana Fulwari Assistant Professor, Department of Business Economics, Faculty of Commerce, The Maharaja Sayajiro University of Baroda, Lokmanya Tilak Road, Sayajigunj, Vadodara 390002, Gujarat, India

The housing finance sector in India has a come a long way from a highly subdued and regulated sector to a vibrant and competitive sector with several players vying for a larger pie. Increased competition is witnessed in the way home loan products are structured and offered. The literature on housing finance too has matured with more in-depth analysis of borrower behavior and determinants of their demand for home loans. In fact, the vast literature on housing finance encompasses several aspects of the multi-dimensional subject and as such can be segregated under broad classification depending upon the dimensions covered. An attempt has been made to bring together studies related to one of the aspects of the housing finance sector and that is, the studies on the determinants of the demand for home loans by borrowers.

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The time-varying effect of interest rates on housing prices.

1. Introduction

2. literature review, 3. research design, 4. empirical analysis results, 5. conclusions, author contributions, data availability statement, acknowledgments, conflicts of interest.

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Lee, C.; Park, J. The Time-Varying Effect of Interest Rates on Housing Prices. Land 2022 , 11 , 2296. https://doi.org/10.3390/land11122296

Lee C, Park J. The Time-Varying Effect of Interest Rates on Housing Prices. Land . 2022; 11(12):2296. https://doi.org/10.3390/land11122296

Lee, Cheonjae, and Jinbaek Park. 2022. "The Time-Varying Effect of Interest Rates on Housing Prices" Land 11, no. 12: 2296. https://doi.org/10.3390/land11122296

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Prediction of Home Loan Status Eligibility using Machine Learning

Proceedings of the International Conference on Innovative Computing & Communication (ICICC) 2022

4 Pages Posted: 27 May 2022

Milind Udbhav

K R Mangalam University

Robin Kumar

Nitin kumar, rohit kumar, dr. meenu vijarania.

K.R. Mangalam University

Swati Gupta

Date Written: May 27, 2022

Getting a home loan and checking the eligibility is not so straightforward process, it is time-consuming for both the housing finance company and the customer who takes the home loan. A lot of time is wasted on the completion of the entire process. Even using an online application does not easy the process as many details are required such as Gender, Marital Status, Education, Dependents, Self Employed or not, Loan Amount, Loan Amount Term, Credit History, Property Area, and Loan Status. The loan status is the target data and others are its parameter through which some output and pattern can be obtained. The company also collects information about the area urban, semi-urban, rural wants customer wants its property. Overcome this issue machine learning is extremely helpful to automate the process to check the eligibility of customers but the fundamental issues that are used over several years do not care about the outliers and machine learning models, which results in less accuracy and affects the overall performance of the model. To reduce this issue the project uses a data cleaning technique for removing null, missing, and repeated values before applying the bivariate and multivariate analysis which helps to categorize the type of data whether it is numericalor categorical for understanding some unique relations and patterns which will help to increase performance and increase the accuracy as well as precision of the model. The machine learning algorithm contains many techniques like Random Forest, decision tree, and many others, but in the project, the two best classification models that are Logistic Regression, and Gradient boosting are used. Logistic Regression is a type of supervised learning which helps for better classification and predicts a discrete value and gradient boosting removes the error and mistakes of the previous model and helps improve overall performance. For automation of the process for eligibility of the customer, a dataset is collected by the housing finance company, then hidden trends and patterns are found which helps build a robust machine learning model. Evaluate the performance of the model performance metrics such as accuracy, precision, and f1score are used. The use of evaluation methods helps to produce the best model which is best to check the eligibility and make this process easier, hassle-free, and convenient. The project uses multiple techniques and multiple methods which makes it a differentiating factor when compared with other models. The use of modern technology not only saves time but also helps in changing the traditional methods and bringing the revolution to improve without compromising the time. Automation also reduces the data cleaner without outliers giving the perfect model which the company wants and can also be further used to change dependents factor for the easy-going process of checking the background details of customers to avoid the chance of getting fraud.

Keywords: Linear Regression, Logistic Regression, Data Cleaning, Univariate Analysis, Bivariate Analysis, Evaluation Metrics

Suggested Citation: Suggested Citation

Milind Udbhav (Contact Author)

K r mangalam university ( email ).

Gurugram India

K.R. Mangalam University ( email )

Sohna Road Gurugram, ID 122103 India

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A PROJECT REPORT ON A COMPARARTIVE STUDY ON HOUSING LOAN OF PUBLIC SECTOR AND PRIVATE SECTOR BANKS (KALYAN AREA

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rekha sharma

IJAR Indexing

Housing finance is what allows for the production and consumption of housing. It refers to the money we use to build and maintain the nation’s housing stock. But it also refers to the money we need to pay for it, in the form of rents, mortgage loans and repayments. House is centre and domestic device for mankind\'s moral and substance development ever since the dawn of civilization. Housing is one of the most important that we human beings need. Adequate housing is essential for human survival with dignity. There are many things that we would find difficult, if not impossible to do without good-quality housing. Housing shortage is an universal phenomenon. It is more acute in developing countries. The housing scenario has become more critical in India in recent years. India has initiated so many housing reform that has taken many forms and manifestations characterized by the reduction in social allocation, cutbacks in public funding and promotion of a real estate culture in close partnership between the state and private actors. Mortgage financing markets can play an important role in stimulating affordable housing markets and improving housing quality in many countries. Unfortunately, these are still in nfancy in India. This lack of development often translates into lower homeownership rates or poor housing quality. Most of these problems stem from the central dilemma that the resources are always too limited and housing development heavily depend on the financial institutions such as banks, credit corporations and development banks for the supply of finance to meet their daily financial needs. Against this backdrop, this paper will assess basic nuance of Indian financing system. Housing Development Finance Corporation Ltd (HDFC) is one of the leaders in the Indian housing finance market with almost 17% market share as on March 2010. Serving more than 38 lakh Indian customers as on March 2011, HDFC also offers customized solutions that fit to the need of the customer. In the FY 2010-11, it registered a net profit of `4528.41 crore. It also registered a net profit of ` 971 crore in the quarter ended September 30, 2011.

Deepak Sonawane

IOSR Journals publish within 3 days

Abstract: The housing finance sector in India has experiences unprecedented change in its structure since its formulation stage of being a solely a government undertaking to a very competitive sector with a large number of financing entities all over India. The paper aims to study the various factors that influence the decision of the consumer for taking Home Loan. The paper focuses on the Home loan offered by LIC and SBI and makes a comparative analysis of the factors that affect the consumers. The paper has a practical implication both for the academicians and for the readers in terms of their concern with the aspect issues regarding factors influencing the buyer behaviour towards Home loans. The paper is original in nature and the highlights of the paper can be used for further research purpose and provides knowledge base to the readers. Keywords: Finance, Home Loan, Interest Rate, LIC, SBI

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Understanding the Impact of Low-Cost Loans on Forced Labor

Approximately 27.5 million individuals fell victim to forced labor in 2021. The Indian construction industry is particularly vulnerable to forced labor as workers experience excessive work hours, required work on rest days, and unpaid wages. Micro-contractors (MCs), who oversee worker environments, frequently struggle with their own financial constraints due to limited access to working capital. This study investigates whether alleviating MC liquidity constraints improves labor conditions for their workers in Bengaluru and Delhi by offering randomly selected MCs access to low-cost loans. Our findings reveal this intervention does not improve working conditions overall; in fact, some outcomes slightly worsen. However, workers employed by more educated and non-migrant treatment MCs experience significantly better labor conditions, underscoring important heterogeneity among MCs. This research offers new causal insights into efforts to combat forced labor.

We are immensely grateful to Aditi Chatterjee, Adam Needleman, all our partners at Sattva Consulting, LabourNet, Gromor, and the Global Fund to End Modern Day Slavery (GFEMS). Funding for the endline survey comes from the United States Department of State Office to Monitor and Combat Human Trafficking under terms of a cooperative agreement through the Human Trafficking Research Initiative (HTRI), managed by Innovations for Poverty Action (IPA). This project was reviewed and approved by the Institutional Review Boards (IRB) at Ashoka University (#22-E-10003-SHARMA) and UCLA (#22-001630). A pre-analysis plan was submitted to the AEA RCT registry (#AEARCTR-0010185). The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. ⓡ The order of authorship was determined using the American Economic Association’s author randomization tool.

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