不動產投資在不動產市場中，是個重要的議題，對建商而言，適當的投資有利於公司經營，對整個不動產市場而言，適當的供給有助於穩定市場，避免供過於求或供給不足。然而目前國內的研究對於不動產投資多集中在收益的討論，則忽略了成本因素。因此為了更全面的討論不動產投資，本文利用財務領域中的托賓Q理論來分析台灣的地區不動產投資，其中定義Q比率的分子為預售屋可能成交市價，分母為每坪租金價格之折現值，同時考慮了不動產投資的收益與成本，然後利用建造執照的多少，作為不動產投資的替代參數，樣本期間為1973年的第二季到2010年的第四季，共151筆季資料，在模型中參考以往文獻，加入許多可能影響不動產投資的因素當作解釋變數。並且進一步利用Hassen (1999) 所提出的門檻模型，以Q比率為門檻變數，建立一個分析不動產投資的門檻模型。接著，實際分析並比較1993-2010台灣五個城市的不動產投資的差異。本文實證研究結果顯示不動產投資與Q比率有正向關係，Q比率可以部分解釋不動產投資的行為，並且以Q比率為門檻變數時，結果具有顯著的門檻效果。在區域的不動產投資行為會視不同區域而受到不同的因素影響。

Housing investment plays a vital role in the real estate market. Although the housing investment has been extensively investigated, the application of Tobin’s Q theory is relatively minor. Hence, the purpose of this study is to apply Tobin’s Q theory to analyze housing investment, using quarterly data for Taipei City from 1973 Q2 to 2010 Q4. The Q ratio numerator is the pre-sale housing price and the denominator represents the value of the rent. The empirical model is estimated by using building permits and use permits as measures of housing investment. Moreover, because the housing market is imperfect, this study applies the threshold regression model to test whether different effects exist in the Q ratio. Finally, this study also compares housing investment in five cities. In conclusion, these findings imply that the Q ratio has a positive relationship with housing investment, as well as a threshold effect. Furthermore, the local housing investments are affected differently by local variables.

論文審定書 i
誌 謝 ii
摘 要 iii
Abstract iv
1. Introduction 1
1.1 Background and information 1
1.2 Research purpose 4
2. Literature Review 6
2.1 Housing supply 6
2.1.1 Elasticity of housing supply 6
2.1.2 Determinants of housing supply 7
2.2 Imperfect housing market 10
2.2.1 Imperfect information 10
2.2.2 Overbuilding 11
3. Theory and Methodology 13
3.1 Theory 13
3.1.1Tobin’s Q theory 13
3.1.2 Imperfect market theory 15
3.2 Methodology 17
3.2.1 Threshold Regression Model 17
3.3 Empirical Model 20
3.3.1 Basic Model 20
3.3.2 Threshold Regression Model 21
3.3.3 Model for regional analysis 22
4. Empirical Results 23
4.1 Data analysis 23
4.1.1 The definition of Variables 26
4.2 Basic model analysis 28
4.3 Threshold effect analysis 32
4.4 Regional analysis 34
5. Conclusion 39
Appendix 41
References 46

1. Agnello, L. and Schuknecht, L. (2011). Booms and busts in housing markets: Determinants and implications. Journal of Housing Economics(3), 20, 171–190.
2. Andrews, D. W. K. (1993). Tests for parameter instability and structural change with unknown change point. Econnometrica, 61(4), 821-856.
3. Arnold, M. (1992). The principal-agent relationship in real estate brokerage services. Real Estate Economics, 20(1), 89–106.
4. Arthur Grimes & Andrew Aitken. (2010). Housing Supply, Land Costs and Price. Real Estate Economics. 38(2), 325–353.
5. Balke, N. S. and Fomby, T. B. (1997). Threshold cointegration. International Economic Review, 38(3), 627-645.
6. Barrot, B. (2006). Empirical studies in consumption, House prices and the accuracy of European growth and inflation Forecasts, Economic Studies 77. Department of Economics, Uppsala University, (No. 98).
7. Biglaiser, G. (1993). Middlemen as experts. RAND Journal of Economics, 24(2), 212–223.
8. Berg, L. and Berger T. (2006). The Q Theory and the Swedish Housing Market—An Empirical Test. Journal of Real Estate Finance and Economics, 33(4), 329–344.
9. Blackley, D. M. (1999). The Long-Run Elasticity of New Housing Supply in the United States: Empirical Evidence for 1950 to 1994. Journal of Real Estate Finance and Economics, 18(1), 25-42.
10. Carroll, W. (1989). Fixed-percentage commissions and moral hazard in residential real estate brokerage. Journal of Real Estate Finance and Economics, 2(4), 349–365.
11. Chan, K.S., 1993. Consistency and limiting distribution of the least squares estimator of a threshold autoregressive model. The Annals of Statistics, 21(1), 520-533.
12. Chan, S. H., Wang, K. and Yang, J. (2012). Presale contract and its embedded default and abandonment options. Journal of Real Estate Finance and Economics, 44(1), 116–152.
13. Dagenais, M. G. (1969). A threshold regression. Econometrica, 37(2), 193-203.
14. Dang, V.A., Kim, M. and Shin, Y. (2011). In search for a robust Method for estimating dynamic panel data models of capital structure. Financial Management Association (FMA) Annual Meeting 2011.( eScholarID:145807).
15. DeCoster, G. P. and Strange, W. C. (2012). Developers, Herding, and Overbuilding.rnal of Real Estate Finance and Economics, 44(1), 7-35.
16. DeLeeuw, F. and Ekanem, N. F. (1971). The supply of rental housing. American Economic Review, 61(5), 806-817.
17. DiPasquale, D. and Wheaton, W. C. (1994). Housing market dynamics and the future of housing Prices. Journal of Urban Economics, 35(1), 1-27.
18. DiPasquale, D. (1999). Why Don't We Know More About Housing Supply? Journal of Real Estate Finance and Economics, 18(1), 9-23.
19. Engle, R. F. and Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251-276.
20. Enders, W. and Granger, C. W. J. (1998). Unit root tests and asymmetric adjustment with an example using the term structure of interest rates. Journal of Business and Economic Statistics, 16(3), 304-311.
21. Enders, W. and Siklos, P. L. (2001). Cointegration and threshold adjustment. Journal of Business and Economic Statistics, 19(2),166-176.
22. Fisher, L. and Yavas, A. (2010). A case for percentage commission contracts: The impact of a “Race” among agents. Journal of Real Estate Finance and Economics, 40(1), 1–13.
23. Follain, J. R. (1979). The price elasticity of the long-run supply of new housing construction. Land Economics 55(2), 190-199.
24. Garella, P. (1989). Adverse selection and the middleman. Economica, 56(223), 395–400.
25. Gonzalo, J. and Pitarakis, J.-Y. (2002). Estimation and model selection based inference in single and multiple threshold models. Journal of Econometrics, 110(2), 319-352.
26. Green, R. K., Malpezzi, S. and Mayo, S. K. (2005). Metropolitan-specific estimates of the price elasticity of supply of housing, and their sources. The American Economic Review, 95(2), 334-339.
27. Hackett, S. (1992). A comparative analysis of merchant and broker intermediation. Journal of Economic Behavior & Organization, 18(3), 299–315.
28. Hansen, B. E. (1996). Inference when a nuisance parameter is not identified under the null hypothesis. Econometrica, 64(2), 413-430.
29. Hansen, B. E. (1997). Approximate asymptotic p-values for structural change tests. Journal of Business and Economic Statistics, 15(1), 60-67.
30. Hansen, B. E. (1999). Threshold effects in non-dynamic panels: Estimation, testing and inference, Journal of Econometrics, 93, 345-368.
31. Hansen, B. E. (2000). Sample splitting and threshold estimation. Econometrica, 68(3), 575-603.
32. Hansen, B. E. and Seo, B. (2002). Testing for two-regime threshold cointegration in vector error correction models, Journal of Econometrics, 110(2), 293-318.
33. Hansen, B. E. (2011). Threshold autoregression in economics. Statistics and Its Interface, 4, 123-127.
34. Hayashi, F. (1982). Tobin’s Marginal q and Average q: A Neoclassical Interpretation. Econometrica, 50(1), 213-224.
35. Hsieh, H. H. Y. (2005). The 1990s Taiwan residential construction boom: a supply side interpretation. Construction Management and Economics, 23(3), 265–284.
36. Hwang, M. and Quigley, J. M. (2006). Economic fundamentals in local housing markets: evidence from US metropolitan regions. Journal of Regional Science, 46(3), 425-453.
37. Ihlanfeldt, K. and Mayock T. (2012). Information, Search, and House Prices: Revisited. Journal of Real Estate Finance and Economics,44(1), 90–115.
38. Jud,G.D. and Winkler,D.T. (2003). The Q theory of housing investment. Journal of Real Estate Finance and Economics, 27(3), 379–392.
39. Kearl, J. H. (1979). Inflation, mortgages, and housing. Journal of Political Economy, 87(5), 1-29.
40. Knoll, M. (1988). Uncertainty, efficiency, and the brokerage industry. Journal of Law and Economics, 31(1), 249–263.
41. Lee, M. L.T., Whitmore, G. A. (2006). Threshold regression for survival analysis: modeling event times by a stochastic process reaching a boundary. Statistical Sciences, 21(4), 501-513.
42. Lee, M. L.T., Whitmore, G. A. and Rosner, B. A (2010). Threshold regression for survival data with time-varying covariates. Statistics in Medicine, 29(7) 896 - 905.
43. Madsen, J. B. (2009). Taxes and the fundamental value of houses. Regional Science and Urban Economics, 39(3), 365–376.
44. Malpezzi, S. and Maclennan, D. (1996). The long-run price elasticity of new residential construction in the United States and the United Kingdom. Working Paper, Department of Real Estate and Urban Land Economics, University of Wisconsin.
45. Mayer, Chris B., and Somerville, C. T. (1996). Land Development and the Supply of New Housing: Unifying Empirical and Theoretical Models of Housing Supply. Working Paper, UBC Centre for Real Estateand Urban Economics.
46. Mayer, C. J. and Somerville, C. T. (2000). Residential construction: using the urban Growth model to estimate housing supply. Journal of Urban Economics, 48(1), 85-109.
47. McDonald, J. F. (2010). The Q theory of investment, the capital asset pricing model & the capitalization rate in real estate valuation. Applied Financial Economics, 20(14), 1133–1143.
48. McNulty, J. E. (1995). Overbuilding, Real estate lending decisions, and the regional economic base. Journal of Real Estate Finance and Economics, 11(1), 37-53.
49. Murray, M. P. (1983). Subsidized and Unsubsidized Housing Starts:1961-1977. The Review of Economics and Statistics, 65(4), 590-597.
50. Murray, M. P. (1999). Subsidized and Unsubsidized Housing Stocks 1935 to1987: Crowding out and Cointegration. Journal of Real Estate Finance and Economics, 18(1), 107-124.
51. Muth, R. (1960). The Demand for Non-Farm Housing. In A.C. Harberger, ed., The Demand for Durable Goods. Chicago: University of Chicago Press.
52. Nordvik, V. (2006). Selective housing policy in local housing markets and the supply of housing. Journal of Housing Economics, 15(4), 279–292.
53. Olsen, E. (1987). The Demand and Supply of Housing Service: A Critical Survey of the Empirical Literature, Handbook of Regional and Urban Economics, Vol. 2. New York: Elsevier.
54. Petruccelli, J. D. and Woolford, S. W. (1984). A Threshold AR(1) Model. Journal of Applied Probability, 21(2), 270-286.
55. Poterba, J. M. (1984). Tax subsidies to owner occupied housing: an asset market approach. Quarterly. Journal of Economics 99(4), 729-52.
56. Rubinstein, A. and Wolinsky, A. (1987). Middlemen. The Quarterly Journal of Economics, 102(3), 581–593.
57. Saiz, A. (2010). The geographic determinants of housing supply. The Quarterly Journal of Economics, 125 (3):1253-1296.
58. Schwab, R. M. (1983). Real and Nominal Interest Rates and the Demand for Housing. Journal of Urban Economics, 13(2), 181-195.
59. Seo, M. H. and Shin, Y. (2010). Inference for dynamic panels with threshold effects and Endogeneity. Economic and Social Research Council, (RES-000-22-3161)
60. Sinai,T. and Waldfogel, J. (2005). Do low-income housing subsidies increase the occupied housing stock? Journal of Public Economics, 89(11), 2137 -2164.
61. Smith, J. K. M. and Brook, C. S. (1999). Threshold autoregressive and Markov switching models: an application to commercial real estate. Journal of Property Research, 16(1), 1-19.
62. Somerville, C. T. (1999). Residential Construction costs and the supply of new housing: endogeneity and bias in construction cost indexes. Journal of Real Estate Finance and Economics, 18(1), 43-62
63. Stover, M. E. (1986). The price elasticity of the supply of single-family detached urban housing. Journal of Urban Economics, 20(3), 331-340.
64. Takala, K. and Tuomala, M. (1990). Housing investment in Finland. Finnish Economic Papers, 3(1), 41–53.
65. Tan, W. (1987). GNP, interest rate and construction investment: empirical evidence from US data. Construction Management and Economics, 5, 185-193.
66. Tobin, J. (1969). A general equilibrium approach to monetary theory. Journal of Money Credit and Banking, 3(1), 15–29.
67. Tong, H. (1977). Some comments on the Canadian lynx data with discussion. Journal of the Royal Statistical Society, Series A, 140(4), 432-436.
68. Tong, H. and Lim, K. S. (1980). Threshold autoregression, limit cycles and cyclical data. Journal of the Royal Statistical Society, Series B, Methodological, 42(3), 245-292.
69. Topel, R. and Rosen, S. (1988). Housing Investment in the United States, Journal of Political Economy, 96(4), 718-740.
70. Tsay, R. S. (1998). Testing and modeling multivariate threshold models. Journal of the American Statistical Association, 93(443), 1188-1202.
71. Tse, R. Y. C. and Raftery J. (2001). The effects of money supply on construction flows. Construction Management and Economics, 19(1), 9-17.
72. Vermeulen, W. and Ommeren, J. V. (2009). Compensation of Regional Unemployment in Housing Markets. Economica, 76(301), 71-88.
73. Yavas, A. (1994). Middlemen in bilateral search markets. Journal of Labor Economics, 12(3), 406–429.
74. Yiu, C. Y. E. and Wong, S. K. (2008). The Effects of Expected Transport Improvements on Housing Prices. Urban Studies, 42( 1), 113-125.
75. Zorn, T. and Larsen, J. (1986). The incentive effects of flat-fee and percentage commissions for real estate brokers. Real Estate Economics, 14(1), 24–47.
76. 林祖嘉(2006)。Tobin，s Q 與住宅投資:台灣的實證分析。行政院國家科學委員會專題研究成果報告(報告編號: NSC93-2415-H-004-016)，未出版。
77. 張金鶚，陳明吉，鄧筱蓉，楊智元 (2009)，台北市房價泡沫知多少？- 房價VS.租金、房價VS.所得，住宅學報(TSSCI)， 第十八卷第二期，1-22。