Investment in Housing Construction: Current Trends and Digital Technologies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Strengthening of such factors as urbanisation, aggravation of the ecological situation, the need to create safe living conditions, increase in building areas, high differentiation in the cost of housing and increase in the scale of social construction, etc. actualises the need to search for innovative forms of corporate investment strategies in housing construction.The purpose of article is to analyse modern trends in the development of investment processes in housing construction.The use of methods of generalisation and system analysis allowed to define the main directions of development of socially responsible investment in the sphere of housing construction; with the help of the method of system-structural analysis the strategy of using BIM-technologies was investigated in detail: essence, stages of implementation, peculiarities of use in different countries.The development of the housing construction industry is under the increasing influence of such external factors as safety, ecology, urbanisation and digitalisation.Corporate investment strategies in the residential construction industry are diversifying: Fix-and-Flip strategy, crowdfunding, investment in property investment funds.New flexible financial mechanisms are emerging, expanding the possibilities of attracting new resources.Modern digital tools make it possible to synthesise the imperatives of greening and smartisation on the basis of BIM (Building Information Modeling) technologies.The advantages of using BIM-technologies are: optimisation of the management and control process, reduction of construction and operation costs, increased coordination of all project participants, reduction of errors and mistakes in project documentation.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it