Bibliographic record
Abstract
Housing occupies about 70 percent of the land area of a typical city. That land area is not randomly distributed, but instead follows regular spatial patterns; these patterns are sectorial and radial (see Hoyt 1939; chapter 2). These geographic patterns form housing submarkets. Specific demographic groups are attracted to housing in those submarkets. As there are many kinds of demographic characteristics of households, there are also many types of housing, and many housing submarkets. Housing submarkets include downtowns, middle-burbs, suburbs; high income; middle income, and low income; new development, mixed use, older development, and mixed new infill with older development; apartments, condominiums; townhouses, high rises, and single-family dwellings. The market analyst makes recommendations on which type of development will be most successful in which submarket and on which submarket would be appropriate for a particular type of development (see Sumichrast and Seldin 1977). Few people today choose to live without the benefit of some type of housing. The choice and availability of what type of housing to live in depends on a complex interaction of many factors, including culture, the natural and built environment, technological scale of society, government, income, stage of life cycle, economics of building construction, and knowledge and imagination of those building the housing. This chapter presents a broad overview of housing market analysis. In the overview, the determinants to demand and supply of housing are presented (See also Harvey, 1992). There is a broad overview of forecasting procedures and methodologies, the methods for projecting absorption rate, housing demand, and competitive supply, and how sales prices and rental prices might be determined. In the last quarter of the nineteenth century, upper-middle-income urban households in the United States and Canada often lived in what are today commonly referred to as Victorian houses. These houses were designed for multigenerational living, including grandparents as the head of household, their children, and their grandchildren. Aunts, uncles, and cousins might have lived in the same dwelling. All the family subunits contributed to the finances of maintaining the house. This provided social security to the elder members of the household, and inexpensive yet high-quality living conditions for the other family members.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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".