The simulation analysis on the accuracy and efficiency of MIT transaction index

The paper aims to investigate the improvement that Ridge regression brings to the commercial real estate index estimation. The MIT transaction based index extends the use of the last model by Fisher, Gatzlaff, Geltner and Haurin (2003.) They address the sample selection bias and the noise effect of the transaction prices in small sample. To correct sample selection bias, MIT transaction index use the Heckman two-step. In addition to the correction of sample selection bias the MIT transaction based index applies the Ridge filters into the index creation procedures to dampen the effect of noise in transaction prices on estimations. Among them, this paper primarily focuses on: (1) investigating error structures, defined by the difference between the true market return and the estimated index return; (2) examining the efficiency improvement of the Ridge Regression as estimation method over Ordinary Least Square (OLS); (3) selecting the proper value of weight factor "k." The findings on the error structures are as follows. First, MIT transaction index does not suffer from smoothing and lagging which is apparent in the appraisal based index. Second, with larger sample size, the estimated market indexes become close to the true market values. In addition, the estimated market index is more biased when it uses small number of sample. Third, the error does not cumulate over time regardless of the sample size. To correct noise effect on the estimation procedure under the observation poor scenario, this study examines the efficiency of the Ridge regression. First, we examine the qualitative form of relationship between ridge coefficients and the control factor of "k." Generally, ridge trace is not significantly changed with respect to "k." It might be argued that the noise effect is not severe in the estimation procedure, yet the introduction of ridge regression apparently improves the estimation. Indeed, between k =4 and 5 ridge coefficients stabilize. Second, to investigate the improvement of ridge regression, we examine the mean square error.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Urban Studies and Planning.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2008

Author dc:creator

• Park, Jungsoo, S. M. Massachusetts Institute of Technology

Advisor dc:contributor.advisor

• William C. Wheaton.

• Urban Studies and Planning.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/45399

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/45399