• 沒有找到結果。

第五章 結論與建議

第二節 後續研究建議

立 政 治 大 學

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l C h engchi U ni ve rs it y

第二節 後續研究建議

本文以交易量及交易冷熱區分類作為研究主體,利用民國 102 年至 104 年台 北市 441 個里共 1323 筆資料,探討影響交易量冷熱分布之原因,其中將影響交 易量之因素分為供給面與需求面,得到需求因素對交易量之影響較供給因素顯著,

且有考量空間相依性之交易冷熱區分類較一般直接以交易量分析之方式為佳。

惟受限資料不足,例如以里為單位之資料較少,或是所得資料目前僅至民國 104 年進而使研究期間限縮,本文建議後續研究方向可分為二:

一、研究時間延長:本文研究之時間範圍僅有三年,若能延長研究期間,所得樣 本數可再增加,得到更一致之結果。

二、研究單位再縮小:本文以里為分析單元,雖較過往研究研究範圍小,相較以 行政區為分析單位,可更準確呈現冷熱區實際分布位置,但實價登錄資料係為點 位資料,尚可利用網格將分析單元再縮小,此法可再探討如特定地區(捷運站、

公園、學校鄰近地區)對房價有正向影響,是否對交易量具有同樣效果。

structural change in the British housing market: A macroeconomic perspective.

Real Estate Economics, 31, 99–116.

25. Anselin, L. (1989). What is Special About Spatial Data? Alternative Perspectives on Spatial Data Analysis (89-4), NY.

26. Anselin, L. &A. Bera (1998). Spatial dependence in linear regression models with an introduction to spatial econometrics. Handbook of Applied Economic Statistics, NY.

27. Can, A. (1992). "Specification and Estimation of Hedonic Housing Price Models." Regional Urban Economics, 22(3), 453-474.

28. Can, A., & Megbolugbe, I. (1997). Spatial dependence and housing price index construction. Journal of Real Estate Finance and Economics, 14, 203–222.

Housing Market: Causality and Co-movements, J Real Estate Finance Economics, 40, 14–40.

30. de Wit, ER., Englund , P., & Francke, MK (2013).Price and Transaction Volume in the Dutch Housing Market. Regional Science and Urban Economics, 43, 220–241.

31. Devaux, N., & Dubé, J. (2016). About the influence of time on spatial dependence: A meta-analysis using real estate hedonic pricing models. Journal of Real Estate Literature, 24(1), 31-66.

32. Dubin, R. A. (1988). Estimation of regression coefficients in the presence of spatially autocorrelated error terms. Review of Economics and Statistics, 70(3), 466–474.

33. Gallin, J. (2006). The long‐run relationship between house prices and income:

evidence from local housing markets. Real Estate Economics, 34(3), 417-438.

34. Hsiao, C. (2014). Analysis of panel data. Cambridge university , Cambridge.

35. Kenny, G. (1999). Modelling the demand and supply sides of the housing market:

evidence from Ireland1. Economic Modelling, 16(3), 389-409.

36. Lamont, O., & Stein, J. C. (1999). Leverage and house-price dynamics in US cities. Rand Journal of Economics, 30, 498–514.

37. Leamer, E. E. (2015). Housing really is the business cycle: what survives the lessons of 2008–09?. Journal of Money, Credit and Banking, 47(S1), 43-50.

38. Lee, C., C., Wang Y., C., & Zeng, J., H., (2016). Housing price–volume correlations and boom–bust cycles, Empir Econ, 52,1423–1450.

39. Malpezzi, S. (2002). Hedonic pricing models: a selective and applied review.

Housing economics and public policy, 67-89.

40. McCarthy, J., & Peach, R.W., (2004). Are home prices the next bubble? Federal Reserve Bank of New York. FRBNY Economic Policy Review, 10, 1–17.

41. Mehmetoglu, M., & Jakobsen, T. G. (2016). Applied Statistics Using Stata: A Guide for the Social Sciences. Sage.

College, NY.

43. Ngai, L. R., & Tenreyro, S. (2014). Hot and cold seasons in the housing market.

American Economic Review, 104(12), 3991-4026.

44. Novy-Marx, R (2009). Hot and Cold Markets, Real Estate Economics. 37(1), 1-22.

45. Quigley, J.M. (1999), “Real estate prices and economic cycles", International Real Estate Review, 1, 1-20.

46. Safer, A., M.,(2002). The Application of Neural Networks to Predict Abnormal Stock Returns Using Insider Trading Data, Applied Stochastic Models in Business and Industry, 18(4), 381-389.

47. Taltavull de La Paz, P., & Gabrielli, L. (2015). Housing supply and price reactions: a comparison approach to Spanish and Italian Markets. Housing Studies, 30(7), 1036-1063.

48. Tsai, I, C., (2014). Ripple effect in house prices and trading volume in the UK housing market: New viewpoint and evidence. Economic Modelling , 40, 68–75.

49. Tsai, I-C., & Peng, C.W., (2010). A Panel Data Analysis for Housing Affordability in Taiwan. Journal of Economics and Finance. 36(2), 335–350.

50. Wang, X., & Varady, D. P. (2005). Using hot-spot analysis to study the clustering of section 8 housing voucher families. Housing Studies, 20(1), 29-48.

51. Wong, S. K., Yiu, C. Y., & Chau, K. W. (2013). Trading volume-induced spatial autocorrelation in real estate prices. The Journal of Real Estate Finance and Economics, 46(4), 596-608.

52. Yava, A. (1994). Economics of brokerage: an overview. Journal of Real Estate Literature, 2(2), 169-195.

‧ 國

立 政 治 大 學

N a tio na

l C h engchi U ni ve rs it y

附錄

隨機效果模型結果表(被解釋變數:各里交易量)

變數 係數

需求 因素

家戶數 -1.60***

所得中位數 2.02*

供給 因素

仲介 -0.41

使照戶數 11.49***

價格 因素

房屋單價中位數 -0.28***

房價變動率 15.36***

常數項 8.05

模型檢定 統計量 P 值

LM test 179.5 0.00 Hausman test 150.27 0.00

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