Introducing Real Estate Assets and the Risk of Default in a Stock-flow Consistent Framework
Bibliographic record
Abstract
The first two chapters are dedicated to the modeling and implementation of a stock-flow consistent framework that incorporates real estate as an asset in the portfolio of the household. The third chapter investigates the main determinants of mortgage repayment of Canadian households. This first chapter presents a five-sector stock-flow consistency growth model where the portfolio decision of the households includes their choice of how much real estate they are interested in holding. The primary aim of the chapter is to model the housing market using the stock-flow consistent approach to explain the current global financial problem triggered by the housing market. The model is then simulated to predict the behaviour of various variables and propose appropriate solutions to the financial problem in the hope of returning the economy to a suitable equilibrium. Households' portfolio consists of money deposits, bills, bank equities and real estate. The other sectors that interact with the household sector are the production firms, the banks, the central bank and the government. Aside from the household sector, the banking sector ends up holding some real estate equivalent to the amount of mortgages defaulted by the households. The supply of real estate from the production sector is therefore augmented by the additional ones held by the banks. The second chapter presents the implementation of the stock-flow consistency model of first chapter. The purpose of the chapter is to run a simulation of the model and experiment with shocks to determine the path of the economic variables of the model. Another objective in performing the experiments is to find policies for mitigating the housing crisis. The model is implemented using the Eviews computer modeling software and runs until a stationary steady state is achieved. Various shocks are applied to the baseline stationary state. The results of the monetary policy show that the mortgage rate shock is more effective in influencing the growth rate of the economy as well as controlling the real estate market. Government fiscal policy is also effective in regulating the housing market. A one-period temporary fiscal policy shock is even capable of generating permanent long run growth effects. Household expectations in future housing price increases or future high rates of housing returns have the effect of heating the real estate market without comparable increases in economic growth. Policy makers must keep these expectations in check. The third chapter analyzes the determinants of mortgage repayment options in Canada. With the freedom that comes with being debt-free and owning a home one will assume that households pay off their mortgages as soon as possible. However, there are factors that inhibit households from carrying out these payoffs. The study uses Canadian micro-level data to examine factors that drive households to default, prepay or continue to make regular mortgage payments. The research methodology uses multinomial (polytomous) logistic regression analyzes. The empirical results establish that the traditional mortgage related predictor variables for repayment are statistically significant with the expected signs. The results relating to the provinces are not significantly different from each other. The results did not however provide any significance in relation to mortgage rates and the number of children in the household.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".