Original research

What actually drives the Punta Cana investor

These findings come from the first doctoral study of investor behavior in Punta Cana — a model that explained 57.4% of investment intention. Not opinion. Evidence.

Watch: a 3-minute overview of the research

A doctoral study, not a sales survey

How the research was done

This platform’s findings come from doctoral research into what actually drives the decision to invest in Punta Cana real estate. The study surveyed real investors and prospective buyers, then analyzed their responses with PLS-SEM — a structural equation modeling method used in serious behavioral research to measure how different factors influence a decision, and how strongly.

The framework extends Ajzen’s Theory of Planned Behavior, the most established model in behavioral research for explaining intentional decisions. Beyond the classic factors, the model added the forces that matter specifically in a tourism investment market: risk perception, tax incentives, overconfidence, herd behavior, and sustainability.

The result is a model that explained 57.4% of investment intention and held up under out-of-sample predictive testing — a substantive result for behavioral research. In plain terms: these aren’t opinions about what investors want. They’re measured, statistically validated findings about what moves the decision.

Method: PLS-SEM with bootstrapping and PLSpredict validation. Theoretical base: extended Theory of Planned Behavior (Ajzen, 1991).

ORIGINAL RESEARCH

What actually drives the Punta Cana investor

of investment intention explained by the model
0 %
Attitude toward the market (β = 0.363)
# 1
behavioral & economic drivers tested
8
doctoral study of investor behavior in Punta Cana
1 st.

Attitude drives the decision. An investor’s overall evaluation of Punta Cana as an opportunity is the single strongest predictor of whether they invest (β = 0.363) — more than social pressure, overconfidence, or perceived control.

Tax incentives work by building confidence. CONFOTUR-type benefits don’t just save money; they shape a favorable attitude that, in turn, drives intention. The fiscal benefit is psychological as much as financial.

Risk awareness makes better investors, not scared ones. Perceiving risk didn’t paralyze investors — it built a more informed attitude and a stronger sense of control (β = 0.570). Investors who understand the risks engage more deliberately.

What doesn’t drive the decision. Social pressure, herd behavior, overconfidence, and even sustainability appeal showed no significant direct effect. The Punta Cana investor decides on substance, not hype.

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Frequently Asked Questions

Addressing key concerns about the investment process.
Q: What is this research, exactly?

A: It’s a doctoral study examining what drives the decision to invest in real estate in Punta Cana. It surveyed investors and prospective buyers and analyzed their responses with PLS-SEM, a structural-equation method used in serious behavioral research. The model explained 57.4% of investment intention — a substantive result for a study of human decision-making.

A: That attitude — an investor’s overall evaluation of Punta Cana as an opportunity — is the strongest direct driver of whether they invest (β = 0.363), ahead of social pressure, perceived control, or overconfidence. In plain terms: people invest when they genuinely believe in the opportunity, not because others are doing it.

A: Not in this study — and that surprised even us. Rather than paralyzing investors, perceiving risk was linked to a more informed attitude and a stronger sense of control over the process (β = 0.570 on perceived control). Investors who engage with the risks tend to decide more deliberately, not less.

A: A meaningful one — but indirectly. Tax incentives didn’t drive intention on their own; they shaped a more favorable attitude, which then drove intention. The fiscal benefit works partly as a psychological signal that the market is a legitimate, supported opportunity, not just a line-item saving.

A: Several that people assume are decisive. Social pressure (subjective norms), herd behavior, overconfidence, and even sustainability appeal showed no significant direct effect on investment intention. The Punta Cana investor in this study decided on substance, not on hype or on what everyone else was doing.

Q: How were the findings validated?

A: Beyond explaining the data, the model was tested for predictive power using PLSpredict and CVPAT — methods that check whether a model can anticipate outcomes for cases it wasn’t built on. The model held up, with strong predictive relevance for investment intention (Q² = 0.457). These are measured, validated results, not opinions.

A: It extends the Theory of Planned Behavior (Ajzen, 1991), the most established framework in behavioral research for explaining intentional decisions. Beyond its classic factors, the model added forces specific to a tourism-investment market: risk perception, tax incentives, overconfidence, herd behavior, and sustainability.

A: Because it tells you what actually moves a sound decision in this market — and what’s just noise. The findings are why this platform focuses on reducing your real risk and clarifying the fiscal picture, rather than pushing urgency or social proof. The research validates the approach: informed investors decide better.

 

A: It’s a doctoral dissertation, completed under academic supervision, defended and approved, by a licensed real estate broker with no property to sell you. That independence is the point — the goal of the research, and of this platform, is objective intelligence, not a sales pitch.

 

A: A summary of the methodology and findings lives on this page, and the full reference list is published here for transparency. If you’d like to go deeper into the model or the data, [join the briefing] and we’ll share more as the research is developed into ongoing investor insights.

References

The findings on this site are grounded in 60 peer-reviewed and scholarly sources.

  1. Abdallah, M. H. I., Al-Tamimi, H. A. H., & Duqi, A. (2021). Real estate investors’ behaviour. Qualitative Research in Financial Markets, 13(5), 728-744. https://doi.org/10.1108/QRFM-06-2019-0071
  2. Afshardoost, M., & Eshaghi, M. S. (2020). Destination image and tourist behavioural intentions: A meta-analysis. Tourism Management, 81, Article 104154. https://doi.org/10.1016/j.tourman.2020.104154
  3. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
  4. Al-Nahdi, T. S., Nyakwende, E., Banamah, A. M., & Jappie, A. A. (2015). Factors affecting purchasing behavior in real estate in Saudi Arabia. International Journal of Business and Social Science, 6(2), 113-125.
  5. Ayodele, T. O., & Olaleye, A. (2022). Fundamental sources of uncertainty in real estate development: Perspectives from an emerging market. International Journal of Construction Management, 22(14), 2775-2787. https://doi.org/10.1080/15623599.2020.1827182
  6. Banco Central de la República Dominicana. (2023). Informe económico anual.https://www.bancentral.gov.do
  7. Barber, B. M., & Odean, T. (2001). Boys will be boys: Gender, overconfidence, and common stock investment. The Quarterly Journal of Economics, 116(1), 261-292.https://doi.org/10.1162/003355301556400
  8. Blonigen, B. A., & Davies, R. B. (2004). The effects of bilateral tax treaties on U.S. FDI activity. International Tax and Public Finance, 11, 601-622. https://doi.org/10.1023/B:ITAX.0000036693.32618.00
  9. Bosnjak, M., Ajzen, I., & Schmidt, P. (2020). The theory of planned behavior: Selected recent advances and applications. Europe’s Journal of Psychology, 16(3), 352-356.https://doi.org/10.5964/ejop.v16i3.3107
  10. Brunnermeier, M. K., & Julliard, C. (2008). Money illusion and housing frenzies. The Review of Financial Studies, 21(1), 135-180.https://doi.org/10.1093/rfs/hhm043
  11. Chiang, T. C., & Zheng, D. (2010). An empirical analysis of herd behavior in global stock markets. Journal of Banking & Finance, 34(8), 1911-1921.https://doi.org/10.1016/j.jbankfin.2009.12.014
  12. Christensen, P. H., Robinson, S. J., & Simons, R. A. (2022). Institutional investor motivation, processes, and expectations for sustainable building investment. Building Research & Information, 50(3), 276-290. https://doi.org/10.1080/09613218.2021.1908878
  13. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates. https://books.google.com/books?id=2v9zDAsLvA0C
  14. Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publications.
  15. Dang, V. H., & Bui, H. V. (2022). Predicting intention to buy real estate for investment in Da Lat city, Vietnam, with an extended theory of planned behavior. Science & Technology Development Journal, 25(2), 2418-2423.https://doi.org/10.32508/stdj.v25i2.3296
  16. Delmendo, L. C. (2024). Dominican Republic’s residential property market analysis 2024. Global Property Guide.https://www.globalpropertyguide.com/latin-america/dominican-republic/price-history
  17. Dunning, J. H., & Lundan, S. M. (2008). Multinational enterprises and the global economy (2nd ed.). Edward Elgar Publishing.
  18. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley.
  19. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.https://doi.org/10.2307/3151312
  20. Global Property Guide. (2025). Dominican Republic’s property market analysis 2025. https://www.globalpropertyguide.com/latin-america/dominican-republic/price-history
  21. Grum, B., & Kobal Grum, D. (2015). A model of real estate and psychological factors in decision-making to buy real estate. Urbani Izziv, 26(1), 82-91.https://doi.org/10.5379/urbani-izziv-en-2015-26-01-002
  22. Gumasing, M. J. J., & Niro, R. H. A. (2023). Antecedents of real estate investment intention among Filipino Millennials and Gen Z: An extended theory of planned behavior. Sustainability, 15(18), 13714.https://doi.org/10.3390/su151813714
  23. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). SAGE Publications.
  24. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). SAGE Publications.
  25. Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report PLS-SEM. European Business Review, 31(1), 2-24.https://doi.org/10.1108/EBR-11-2018-0203
  26. Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: Updated guidelines. Industrial Management & Data Systems, 116(1), 2-20.https://doi.org/10.1108/IMDS-09-2015-0382
  27. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135.https://doi.org/10.1007/s11747-014-0403-8
  28. Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. Advances in International Marketing, 20, 277-319.https://doi.org/10.1108/S1474-7979(2009)0000020014
  29. Hernández-Sampieri, R., & Mendoza Torres, C. P. (2023). Metodología de la investigación: Las rutas cuantitativa, cualitativa y mixta (2da ed.). McGraw-Hill Interamericana.
  30. Judge, M., Warren-Myers, G., & Paladino, A. (2019). Using the theory of planned behaviour to predict intentions to purchase sustainable housing. Journal of Cleaner Production, 215, 259-267.https://doi.org/10.1016/j.jclepro.2019.01.029
  31. Kabir, S., Jamal, Z. B., & Kairy, B. (2023). How much to invest for house purchase? The consumer purchase intention perspective of real estate investment decision. International Journal of Housing Markets and Analysis, 17(4), 881-898.https://doi.org/10.1108/IJHMA-10-2022-0151
  32. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291.https://doi.org/10.2307/1914185
  33. Kock, N., & Lynn, G. S. (2012). Lateral collinearity and misleading results in variance-based SEM: An illustration and recommendations. Journal of the Association for Information Systems, 13(7), 546-580. https://doi.org/10.17705/1jais.00302
  34. Liengaard, B. D., Sharma, P. N., Hult, G. T. M., Jensen, M. B., Sarstedt, M., Hair, J. F., & Ringle, C. M. (2021). Prediction: Coveted, yet forsaken? Introducing a cross-validated predictive ability test in partial least squares path modeling. Decision Sciences, 52(2), 362-392.https://doi.org/10.1111/deci.12445
  35. Lieser, K., & Groh, A. P. (2014). The determinants of international commercial real estate investment. Journal of Real Estate Finance and Economics, 48(4), 611-659. https://doi.org/10.1007/s11146-012-9401-0
  36. Lynn, M. R. (1986). Determination and quantification of content validity. Nursing Research, 35(6), 382-385. https://scispace.com/pdf/determination-and-quantification-of-content-validity-2xqxi6h2xb.pdf
  37. Ma, K. V., Dang, N. T., & Bui, H. T. M. (2023). Predicting the determinants of investors’ intention to purchase tourism real estate property: An extended TCP approach. Review of Integrative Business and Economics Research, 12(4), 102-117.
  38. Murray, C., Ween, C., Torres-Romero, Y., & Ramirez, Y. (2020). Real estate in Central America, Mexico and the Caribbean. Routledge.
  39. Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
  40. Olapade, D. T., Ayodele, T. O., & Olaleye, A. (2018). Impediments to foreign real estate investment in an emerging market: A tripartite characterization of the Lagos, Nigeria property market. Journal of Property Investment & Finance, 36(5), 479-494. https://doi.org/10.1108/JPIF-12-2017-0084
  41. Škrabić Perić, B., Smiljanić, A. R., & Kežić, I. (2022). Role of tourism and hotel accommodation in house prices. Annals of Tourism Research Empirical Insights, 3(1), Article 100036.https://doi.org/10.1016/j.annale.2022.100036
  42. Polit, D. F., & Beck, C. T. (2006). The content validity index: Are you sure you know what’s being reported? Critique and recommendations. Research in Nursing & Health, 29(5), 489-497.https://doi.org/10.1002/nur.20147
  43. Poon, J. (2017). Foreign direct investment in the UK real estate market. Pacific Rim Property Research Journal, 23(3), 249-266.https://doi.org/10.1080/14445921.2017.1372038
  44. Ramasubbian, H., Priyadarsini, K., & Vasuki, M. (2018). Investment decisions in real estate: A theory of planned behavior (TPB) based approach. International Journal of Pure and Applied Mathematics, 119(17), 2377–2381. https://acadpubl.eu/hub/2018-119-17/2/196.pdf
  45. Ringle, C. M., & Sarstedt, M. (2016). Gain more insight from your PLS-SEM results: The importance-performance map analysis. Industrial Management & Data Systems, 116(9), 1865-1886.https://doi.org/10.1108/IMDS-10-2015-0449
  46. Ringle, C. M., Wende, S., & Becker, J.-M. (2024). SmartPLS 4. SmartPLS.https://www.smartpls.com
  47. Sarstedt, M., Ringle, C. M., & Hair, J. F. (2021). Partial least squares structural equation modeling. In C. Homburg, M. Klarmann, & A. Vomberg (Eds.), Handbook of market research (pp. 587-632). Springer.https://doi.org/10.1007/978-3-319-57413-4_15
  48. Sehra, S. K., Godwin, B. J., & George, J. P. (2022). Are expensive decisions impulsive? Young adults’ impulsive housing and real estate buying behavior in India. International Journal of Housing Markets and Analysis, 17(2), 266-286.https://doi.org/10.1108/IJHMA-06-2022-0090
  49. Shmueli, G., Ray, S., Velasquez Estrada, J. M., & Chatla, S. B. (2016). The elephant in the room: Predictive performance of PLS models. Journal of Business Research, 69(10), 4552-4564.https://doi.org/10.1016/j.jbusres.2016.03.049
  50. Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J.-H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322-2347.https://doi.org/10.1108/EJM-02-2019-0189
  51. Singh, A., Kumar, S., Goel, U., & Johri, A. (2024). Predictors of investment intention in real estate: Extending the theory of planned behavior. International Journal of Strategic Property Management, 28(6), 349-368.https://doi.org/10.3846/ijspm.2024.22234
  52. Škrabić Perić, B., Rimac Smiljanić, A., & Kežić, I. (2022). Role of tourism and hotel accommodation in house prices. Annals of Tourism Research Empirical Insights, 3(1), Article 100036. https://doi.org/10.1016/j.annale.2022.100036
  53. Sondari, M., & Sudarsono, R. (2015). Using theory of planned behavior in predicting intention to invest: Case of Indonesia. International Academic Research Journal of Business and Technology, 1(2), 137-141.
  54. Stylidis, D., Shani, A., & Belhassen, Y. (2017). Testing an integrated destination image model across residents and tourists. Tourism Management, 58, 184-195. https://doi.org/10.1016/j.tourman.2016.10.014
  55. Triandis, H. C. (2001). Individualism–collectivism and personality. Journal of Personality, 69(6), 907-924.https://doi.org/10.1111/1467-6494.696169
  56. Trinh, T. H. (2022). Theoretical foundations of real estate market behavior. Cogent Business & Management, 9(1), Article 2132590. https://doi.org/10.1080/23311975.2022.2132590
  57. Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124-1131.https://doi.org/10.1126/science.185.4157.1124
  58. Vuković, M. (2024). Generational differences in behavioral factors affecting real estate purchase intention. Property Management, 42(1), 86-104.https://doi.org/10.1108/PM-11-2022-0088
  59. Yanz, Y., & Ming, C. W. (2024). Understanding consumer decision-making in real estate: An integrative analysis using the theory of planned behavior. Journal of Digitainability, Realism & Mastery (DREAM), 3(08), 33-46. https://doi.org/10.56982/dream.v3i08.264
  60. Zhang, L., Wang, H., Tian, X., & Zhang, W. (2020). Understanding consumers’ purchase intention for brownfield redevelopment housing: An extended theory of planned behavior model. Journal of Cleaner Production, 249, 119407.https://doi.org/10.1016/j.jclepro.2019.119407