Thesis Archive

A strategic pricing model using a general demand function with micro and macroeconomic factors for applying real options in real estate investments (2007)

Steve Lester N. Chan
Kristine P. Chua
Stephanie Grace M. Sonsing

Abstract:

-The real estate industry has been changing rapidly in terms of innovation, progress, and financing especially in the Philippines since fifteen years ago. As these changes continue, real estate developers have also continuously formulated ways to balance price and demand in order to gain higher profits. The concept of the study was brought about by the lack of existing real estate pricing model in the Philippines that can consider dynamic changes with its changing environment, and that can ultimately enable the real estate developer to gain the maximum possible revenue.

The model was developed in a three-part progression: 1) the general model variables and parameters are identified, 2) the objective function will be built, and then 3) the constraints will be developed. Real option is incorporated through the sensitivity analysis.

The results of the model runs are analyzed using the two general types of real options, which are growth option and flexibility option. General conclusions that can be made from the initial run of the model is that options to delay sale (flexibility option) is important in decision to sell in real estate. In all cases of the model runs, delay in sale is made when prices are lower instead of pushing the model to sell more at lower prices to generate profit. With the delay in sale considered, the model chose to sell the number of units at a price that a buyer is willing to purchase that unit at the highest value.

In the model validation, the study made a multiple linear regression analysis using STATISTICA and GAMS. The regression results proved that depending on the category of the project (i.e. the property size), the demand factors vary. The inclusion of the factors was analyzed using their beta values in order to see the extent of their effects on the demand. The effects of these factors to demand are quantified during the multiple linear regression analysis. The validation process was able to quantify the effects of growth options such as road works construction, urbanization of city location in pricing and selling decisions of the model. It also measured the different changes in price and selling options when flexibility options in the time of sale of a property unit are included in the model. The results of the validation confirmed that the delay in sale contribute did indeed increase profits under the following scenarios: increased land area availability, increased accessibility in 3rd year, changed type of project from a simple to a commercial subdivision, changed values for adjacency, accessibility and city tax rate, and future urbanization of city.

Results generally yielded higher sales and prices for the year that the increase in accessibility is achieved. Another growth option tested was that of developing the property from a simple subdivision to a commercialized one. It can be concluded both from the regression analysis and validation that these types of properties are more attractive when located further away from the urbanized cities. House and lots located further away from Manila are currently attracting the population. Regression analysis also showed pointed out that the growth of the Philippine peso decreases attractiveness of real estate properties. This means that OFW remittances and foreign investments play an important role in the success of this category in the industry.

Real options theory inclusion in the model was able to consider the possibility of the developing land concept (wait for future options) under flexibility options in profit maximization. With higher prices, sales can only go so far, but this does not mean it is put to waste. If this was not considered in the computation, a site plan layout of the lot area would have been submitted already. Since alteration can no longer be done, the investor will have to push its sales by lowering the prices.

Since the R-values of property unit 1 and property unit 2 data were highly correlated, recommendations for further study include increasing the range of cross-sectional and time-series data, performing a nonliniear regression analysis, and considering pre-selling projects such as condominiums and high-class subdivisions.

Adviser:

Mr. Dennis Cruz
[email protected]