The main aim of this thesis is to design a new and more advanced methodology for valuation of real estate portfolios and incorporate uncertainty into the valuation process. From the comprehensive real estate literature we identified the main value drivers whose treatment is often neglected in the traditional appraisal methodology as they are used as a single point estimates. The identified parameters are the discount rate, inflation, prime rent, occupancy and market capital value changes. In contrast with the traditional approach, we calibrate distributions of these parameters from historical data and allow their variation through the Monte Carlo simulation. This enables us to model their impact on the market value of our real estate portfolio, which comprises of A-class office buildings with detailed property level data including their lease structure. The methodology presented here builds on the widely used DCF approach, which is augmented by the risk parameters and through the thousands of iterations of the Monte Carlo simulation we arrive to a distribution of all potential values of the portfolio. Finally, the knowledge of relevant risk factors and their impact on returns of their property portfolio then provides investors with better and more reliable foundations for their decisions and...
| Identifer | oai:union.ndltd.org:nusl.cz/oai:invenio.nusl.cz:347828 |
| Date | January 2015 |
| Creators | Koubková, Klára |
| Contributors | Parrák, Radovan, Maršál, Aleš |
| Source Sets | Czech ETDs |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/masterThesis |
| Rights | info:eu-repo/semantics/restrictedAccess |
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