Only the Best? Exploring Cross-Border Investor Preferences in US Gateway Cities
Bibliographic record
Abstract
Despite heady growth in cross-border investment into commercial real estate over recent decades, there are few studies that examine differences in investment preferences between domestic and cross-border investors at a micro level. We address the gap by examining the characteristics of assets acquired by cross border investors in six major US metro areas, comparing them with the purchases made by US investors in those same areas. Our study uses data on more than 67,500 transactions recorded by Real Capital Analytics (RCA) over the period from Q1 2003 to Q3 2016. As well as examining cross-border investors in aggregate, we isolate and examine purchases by investors from each of the four principal source nations for cross-border real estate investment in these cities. This is important since treating cross-border investors as a single group may obscure important differences between them. We employ multilevel logit techniques and we find across a number of specifications that cross-border investors prefer larger assets, newer assets and CBD locations regardless of nationality. However, temporal and sectoral patterns of investment, as well as evidence for return chasing behavior, vary with the nationality of investor being studied.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".