Estimating the Impact of the Interest Rate on the Swedish Real Estate Market
Bibliographic record
Abstract
During a large part of the 2010s Swedish economy was heavily influenced by abnormally low interest rates, which even reached negative territory during several years. This occurrence affected the real estate industry by enabling cheap financing options, thus leading to an expansion in the companies’ debt level. When the interest rates began rising sharply in 2022 the question arose whether real estate firms would be able to uphold a healthy profitability level, or if the returns would be diminished due to the higher cost of debt. This thesis aims to investigate the impact of the interest rate on Swedish real estate companies’ profitability measured by return on equity. Nineteen Swedish real estate companies were analyzed over the period Q1 2015 – Q1 2024 by conducting a quantitative analysis through an Autoregressive Distributed Lag (ARDL) model and a panel data regression model, both of which accounts for short-term and long-term dynamics of the interest rate’s impact over time. The analysis was also divided into two distinct time frames, one with negative interest rates (Q1 2015 – Q1 2022), and one with quickly rising interest rates (Q2 2022 – Q1 2024), in order to be able to compare the impact of the interest rate on the profitability level during different macroeconomic conditions. The capture of time-dependencies was enabled by using both a one quarter lag and a four quarter lag on both the dependent variable and the independent variables. The findings suggest that over all analyzed time frames the level of return on equity is significantly negatively affected by the policy interest rate; however, the interest rate shows a significant lag of influence, indicating that the interest rate level from both one quarter and four quarters ago has an impact on the current level of return on equity. Also, the prior level of profitability significantly correlates with the current return of equity among real estate companies, in both the short term and the long term, which indicates the presence of a momentum effect. The relationship is positive during all time frames and for all lags, except during the Q2 2022 – Q1 2024 time span utilizing one quarter’s lag, where the connection is negative.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".