Real Estate as Investment Instruments in Poland Between 2020 and 2023
Notice bibliographique
Résumé
Theoretical background: The article constitutes an analysis of the macroeconomic situation from 2020 to the first quarter of (Q1) in relation to real estate investments. Factors such as the COVID-19 pandemic, the influx of refugees to Poland, and rising inflation had a significant impact on investor behavior in the discussed market. The dynamic macroeconomic situation also led to changes in consumer and tenant preferences, while significantly affecting demand and supply. The years 2020–2023 (Q1) were characterized by a distinct specificity that accentuated the risks associated with real estate investments. This emphasized clear differences in various categories between real estate as investment assets and funds, securities, or other alternative investment methods. Purpose of the article: The aim of the article is to identify the risks associated with real estate investment in Poland during the period from 2020 to 2023 (Q1). It is also crucial to describe the macroeconomic aspects and their impact on the investment real estate market during the mentioned period. Research methods: The article presents the results of an empirical study aimed at identifying factors contributing to the increase in risk associated with real estate investment in the years 2020–2023 (Q1). Additionally, it was significant to verify the financing methods for the purchase of investment properties by the survey participants. The study was based on a survey questionnaire. A total of 250 respondents participated in the study, expressing their willingness to purchase their first or subsequent property for investment purposes during the years 2020–2023 (1Q). Respondents were asked 7 questions, including those related to factors influencing or hindering the completion of investment property transactions in the years 2020–2023 (Q1), as well as the method of financing the purchase and the reason why respondents utilized borrowed capital or relied solely on their own funds. Furthermore the article employed a research methodology that included critical analysis of secondary sources, as well as methods such as analysis, synthesis, description, deduction, induction, and reduction. The Polish and foreign literature related to investment and investment properties was subjected to analysis. Additionally, laws, resolutions, regulations, scientific works from other organizations, and reports were studied, serving as valuable sources of information about the real estate market situation from 2020 to 2023 (Q1). In formulating recommendations, a generalizing-synthesizing method was employed (deduction, reduction, induction). The inductive method aided in analyzing the significance and characteristics of investment properties. On the other hand, the deductive method was used to analyze the issue starting from the macroeconomic situation in Poland and delving into the changes in the investment real estate market. By making a critical analysis of the literature, the reduction technique was applied, thus, verifying the previously formulated hypotheses. Main findings: Based on the conducted analysis, it can be observed that the return on equity (ROE) from real estate investments takes on a negative value when investors partially use borrowed capital. This situation is influenced by high interest rates. However, alongside this factor, there is also a social element related to the high demand for property rentals. In the case of investors investing solely from their own sources, this leads to a return on equity ranging between 2.7% and 3.6% during the analyzed time period. The conducted analysis reveals that macroeconomic, social, and legal factors dynamically shape the demand and supply in the real estate market during the period from 2020 to 2023 (Q1). This is closely related to the decisions of investors aiming to invest their capital in investment properties.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».