A forecast of Commercial Real Estate Development and Investment Volumes in the Czech Republic
|Thesis title in Czech:||Prognóza rozvoje komerčních nemovitostí a investičních objemů v České republice|
|Thesis title in English:||A forecast of Commercial Real Estate Development and Investment Volumes in the Czech Republic|
|Key words:||komerční nemovitosti, investice, investiční objemy, výnosy z prémiových nemovitostí, kancelářské budovy, nákupní střediska, logistické haly, Česká republika|
|English key words:||commercial real estate, investment, investment volumes, prime yields, office properties, shopping centers, industrial properties, Czech Republic|
|Academic year of topic announcement:||2015/2016|
|Type of assignment:||diploma thesis|
|Department:||Institute of Economic Studies (23-IES)|
|Supervisor:||Bc. Tomáš Jandík|
|Author:||hidden - assigned by the advisor|
|Date of registration:||12.07.2016|
|Date of assignment:||12.07.2016|
|Date and time of defence:||18.09.2018 08:30|
|Venue of defence:||Opletalova - Opletalova 26, O314, Opletalova - místn. č. 314|
|Date of electronic submission:||31.07.2018|
|Date of proceeded defence:||18.09.2018|
|Reviewers:||PhDr. Pavel Streblov|
|1. Ball, Michael, Colin Lizieri, and Bryan D. MacGregor. The economics of commercial property markets. London: Routledge, 1998. Print.
2. Brooks, Chris, and Sotiris Tsolacos. Real Estate Modelling and Forecasting. Cambridge, UK: Cambridge UP, 2010. Web.
3. Brueggeman, William B., and Jeffrey D. Fisher. Real estate finance and investments. McGraw-Hill Irwin, 2011. Print. Fourteenth edition.
4. Cushman & Wakefield, "Commercial Real Estate Research.". N.p., n.d. Web. 15 June 2016.
5. Geltner, David, and Norman G. Miller. Commercial Real Estate: Analysis and Investments. Cincinnati, OH: South-Western Pub., 2001. Print.
6. Lieser, Karsten, and Alexander Peter Groh. "The Attractiveness of 66 countries for Institutional Real Estate Investments: A composite index approach." N.p., July 2010. Web. 6 Dec. 2016. <http://www.iese.edu/research/pdfs/DI-0868-E.pdf>. Working Paper.
7. Lieser, Karsten, and Alexander Peter Groh. "The Determinants of International Commercial Real Estate Investments." IESE Business School, University of Navara, July 2011. Web. 15 Dec. 2016. http://www.iese.edu/research/pdfs/DI-0935-E.pdf. Working Paper.
8. Obstfeld, Maurice, and Kenneth S. Rogoff. Foundations of International Macroeconomics. Cambridge, MA: MIT, 1996. Print.
9. Steering and Advisory Committees of the Shaping the Future of Real Estate - Asset Price Dynamics Initiative. "Understanding the Commercial Real Estate Investment Ecosystem: An Early Warning System Prototype." World Economic Forum, Feb. 2016. Web. 20 Nov. 2016. < http://www3.weforum.org/docs/WEF_IU_Understanding_the_Commercial_Real_Estate_Investment_Ecosystem.pdf>.
|Preliminary scope of work|
|This thesis attempts to forecast investment volumes of the commercial real estate market in the Czech Republic in the medium-term from 2017 to 2020, using both qualitative methods and econometric models. Fundamental analysis and chart analysis are employed while judgemental forecasts by market experts are collected. In order to find evidence of historical and upcoming commercial asset price bubbles, a state-of-the-art peak-tagging technique and chart analysis for the graph of commercial real estate capital value index are used before integrating with market specialists’ opinions. Neither CRE bubble nor signal for future major downturn has been found since 2000 despite the occurance of minor overpricing periods. ARIMA and several VAR models with endogenous and exogenous variables are run to find the best quantitative forecasts. Final forecasted investment volume for the upcoming years is found by integrating experts’ opinion and results from the chosen model.|
|Preliminary scope of work in English|
|In the Czech Republic, there are only articles and research papers by private firms and other organisations address this topic employing only judgemental and chartist forecasts. Suggested by many researchers and evidence from UK market practice, particularly Brooks et al. (2010) a combination of judgemental method and model-based approach is recommended to perform any forecast regarding real estate due to is complexity and dependence on a wide range of factors. All things considered, I will follow this suggestion, and then perform forecast tests for the results of each method.
1. Qualitative method:
• Pairwise historical movement graphs.
• CRE investment market, even though comprises of mainly existing properties, also depend on the construction market. Construction of such large-scaled properties requires several years to complete and usually require financing and other decision-making in advance.
• Consumer demand, investor mix, business cycle detection, macro-economic performance, trend analysis, building permits issued…
• Analysing and drawing information from the graph of the historical CRE investment volume in Czech and the CRECVI index (Commercial Real Estate Capital Value Index, developed by the research department of a leading agency Cushman & Wakefield Czech Republic). This index in its raw form are based on nominal values and not adjusted to varying risk-free rate, therefore I would like to solve these drawbacks.
- Analysing other qualitative factors which are specific for Czech market: investors’ sentiments towards investing in Czech over the years, current standpoint of Czech CRE market, long-term fundamental potential, and current constraints in terms of planning permit issuance.
- Judgemental: Summarizing market experts’ forecasts and opinions, along with their explanations, based on official announcements such as periodical research papers, press releases, relevant publishing media. “Market experts” are the largest consultancy corporations, investors, banks and developers who are closely related to the Czech CRE market. I will assess briefly the possibility of these views being somehow biased, since most of them are businesspeople that prefer publishing positive views to spur their activities and boost the market as a matter of fact.
2. Quantitative method, or model-based approach:
- At least one of the following two successful models will be calibrated:
• An augmented random effect regression: this model identifies determinants of International Commercial Real Estate investment with a panel data of 47 countries around the world from 2004-2009 (Lieser et al., 2011). The working paper is very comprehensive with 66 data series proxied by 6 latent key drivers. Besides that, 22 main indicators for country-based time series estimator were pointed out, based on which I have a clear direction of how to choose relevant variables for the case of the Czech Republic. Interpretations on the results of “within” estimator will be particularly focused since they imply correlations within a country over time. 80% of data required are available for the Czech Republic in annual terms, which are assumed to be of enough amounts for calibration.
• The Early Warning System prototype: written by a number of researchers at Steering and Advisory Committees of the Shaping the Future of Real Estate. Even though the study is thorough with accurate results, most of the data types are typical for the USA, which might not be available for Czech.
Necessary tests will be run and then future estimated values will be used to calibrate.
- My model: VAR model is chosen due to its suitability for forecasting purposes. This method requires less information about explanatory variables when compared to structural models having simultaneous equations; and in fact the economic-political-social factors are dynamically interrelated. Impulse response, as a result of VAR, will be analysed and interpreted.
• Stability issue: Detect stationary problem by unit root tests. To avoid this problem, differences or percentage changes are used. According to several literature, annual changes (p=4, i.e. lag of 4 quarters) are proved to be suitable and better than first differences.
• Lag length selection: either based on literature review or by statistical testing (where we set a null hypothesis, for example H0: p=4 and test it against the alternative H1 : p > 4; then asymptotic likelihood test will be construct and repeated until the optimal lag length is found).
• Choice of variables: this is the most challenging part of VAR, 22 variables suggested by Lieser and Groh (2011) will be scrutinised to finalise the selection of variable set, with much consideration regarding the Czech market. The following might be the most essential variables: changes in government 10Y bond yields, interest rate spread, logarithm of change in prime yield (specific to each CRE subsector), consumer spending, GDP growth, inflation, industrial production, and unemployment rate. In the end, only 4-5 variables will be selected.
• Ordering of variables: from the most exogenous variables to the most vulnerable variables.
- Prognosis analysis: using simple trend-line analysis. In particular, data up to Q2 2016 (the start date of research) will be used for the models; then the trend-line will be applied to find the future values; finally real data collected till Q1 2017 (the end date of research) will be used to cross-check the findings.
- Forecast tests: Loss function will be used. It measures the magnitude of forecast error which is resulted by subtracting actual values to forecast values. If necessary, revision of VAR model will be done.
Conclusion: sum up results from all aforementioned methods and draw conclusion based on forecast tests.