The construction industry's two main certifications are Leadership in Energy and Environmental Design (LEED) and Energy Star. To achieve the triple bottom line of sustainability for these certifications, both certifications should make a positive impact individually as well as mutually, with their impact extending to the surrounding neighborhood. This research examined the spillover effect of LEED and/or Energy Star certified office buildings on the property values of other buildings in their neighborhood in Manhattan, New York City from an economic standpoint. The spatial analysis approach using the Geographic Information System and the statistical analysis approach based on the Hedonic Price Model and the Linear Mixed Effect Model were applied to identify the geographical distribution of LEED and/or Energy Star certified office buildings and their other buildings in their neighborhoods and analyze the impact of the former on the latter. The results were as follows: 1) There was a significant correlation between a LEED and/or Energy Star certified office building and the unit market values of its adjoining buildings through the unit market values of the certified office building, the LEED and/or Energy Star certification achievement, and the major features of LEED certification; 2) There was a varying spillover effect of the certified office building on the median unit market value of buildings depending on their proximities to a LEED and/or Energy Star certified office building. This research provides a firm foundation for further efforts to quantify the spillover effect of LEED and/or Energy Star certification on a neighborhood from an economic standpoint, thus supporting and encouraging growth in the local real estate market and benefitting not only the owners, developers, and investors of the certified office building but also the owners of neighboring buildings. / Ph. D.

收益資本化率是不動產估價收益法中最為關鍵的因素，不動產淨收益透過收益資本化率轉換為價值，其步驟看似簡易，卻深藏「失之毫釐，差之千里」的陷井。在國內，商用不動產廣泛運用收益法來估計不動產價值，但收益資本化率之研究卻相當缺乏，相關文獻僅限於國外，因此，本研究以台北市商用不動產為主要研究對象，除了探討收益資本化率之影響因素與實務界在決定過程上的缺失外，並提出較具有解釋力的計算與預測方法。 本研究蒐集自1992年至2008年止台北市商用辦公室之租金與房價資料，利用特徵價格模型將轉換後的租金與房以直接資本化法計算收益資本化率，與問卷結果中可得之影響因素資料進行相關分析、多變量自我迴歸分析，找出關鍵之影響因素，並利用指數平滑法、Box-Jenkins、灰色與VAR模型等時間預測方法來進行收益資本化率之預測。 本研究實證結果顯示，以特徵價格模型計算台北市商用辦公室之收益資本化率，其模型解釋力相當高，並獲致區位上的差異結果。此外，影響因素分為四個面向包含：社會經濟條件、其他投資工具、市場條件、建築物條件等四項，影響因素中除了收益資本化率自身前期為一個重要指標，社會經濟條件之經濟成長率、建築物條件之台電用電不足底度戶數與市場條件之電力(企業)總用電量(十億度)為重要領先指標，其他投資工具之股價指數與社會經濟條件之失業率為同步對收益資本化率有影響性，代表市場條件之不動產景氣領先綜合指標受到收益資本化率之影響而為落後指標，而收益資本化率本身並具有平均數復歸的現象。在未來趨勢的預測方面，單一時間數列適合長期資料之預測模型Box-J進行預測之解釋力（adjR-square）較高，各模型之預測結果平均來說，以Box-J與VAR-I為最有可能之預測值，而最樂觀為VAR-II之預測值，最悲觀為灰色之預測值。 透過國內不動產估價實例進行驗證，本研究結果可以準確進行事後驗證與事前預測，對於提升不動產估價師與投資者收益資本化率的估計與解釋能力上應有所助益。 / Capitalization rate is the most crucial factor in the income approach real estate appraisal method. When this appraisal method is applied, real estate net income is converted into value via capitalization rate. The conversion process is simple and straight forward, but there seems a trap of misuse in real practice. Domestically, income approach real estate appraisal method has been put to use extensively to estimate commercial real estate value. However, research in capitalization rate is in shortage considerably. Related literature merely re-stricted in overseas resource, Hence, our study focus on Taipei office as the main subject to investigate the influential factors of capitalization rate and its shortcoming of real practice, in additional, we propose more concrete explana-tory calculation and estimation methods. Our study collects information on Taipei City office rental and transaction price between 1992 and 2008. Using Hedonic Price Model, the converted rents and transaction price are applied by direct Capitalization Approach to calculate capitalization rate. Thereafter, we compared with the influential factors derived from the survey through correlation analysis, and Vector Autoregression Model (VAR) to find the pivotal influence factors. Meanwhile, we use Exponential Smoothing(ES)、Box-Jenkins、Grey Forecasting Model and VAR time-series estimate method to conduct capitalization rate prediction. The result of this study provides actual proof that to use Hedonic Price Model to calculate office capitalization rate of Taipei, the results are rather ac-curate and indicate a geographical difference. In terms of influential determi-nates, the capitalization rate in itself is an important indicator in the beginning. The Economical Growth Rate and the household under electricity base degree utility of Taiwan Power Company are also significant leading indicators. Stock Price Index, Unemployment Rate and the capitalization rate affect among them. Real Estate Cycle Leading Indicators are affected by capitalization rate and hence a lagged index. Capitalization itself exhibits mean return phenomenon. The future trend prediction of capitalization rate, Box-J and VAR-I give the most likely estimates; VAR-II gives the most optimistic value, whilst Grey Forecasting Model gives the most pessimistic estimation. Through Case study of domestic real estate appraisal to conduct experi-mental verification indicates that the result of our study can accurately carry out aftermath verification and prior estimation. As such, hopefully our empiri-cal study might be able to benefit real estate appraiser and investor to enhance their ability to determine the capitalization rate.

近年來影城結合商場、娛樂及餐飲，如雨後春筍般出現，建商售屋亦常以影城為吸睛廣告，對消費者而言，影城對周邊住宅價格影響是否存有關聯性係其購屋選擇關心條件之一，然而現今影城的類型大不相同，且觀察到影城大多座落於商圈內或是與百貨商場結合。因此，本研究將影城、商圈與住宅價格的相關程度做交叉分析，以初步了解其關聯程度，並應用集群分析以控制異質樣本，使得形成同質性的房屋屬性來看影城效果，再透過複迴歸模型分析，探討不同類型的影城、商圈與周邊住宅價格之影響關係。 透過複迴歸模型實證結果得知，影城對台北市周邊住宅價格是有影響的，以總價1,000萬元房屋，平均而言，正向的價差介於41萬元至310萬元之間，負向的價差則介於30萬元至271萬元之間，並且正向的價差高於負向的價差。進一步將影城依經營模式及服務方式分類進行實證，採連鎖經營模式的影城周邊房價價差約為40萬元，而提供複合式服務或僅提供單一式服務的影城，對房價的影響差別不大。該結果可提供消費者在評估購買房屋時之參考，亦可作為開發商與銷售業者在預售屋訂價策略及廣告遵循原則。 此外，由於消費型態的改變，消費者習慣將看電影結合其他休閒活動，這些由百貨公司以及電影院異業結盟的商圈更得消費者青睞，影城進駐與商圈發展之關係相輔相成，進而影響新北市周邊住宅價格，以總價1,000萬元房屋，平均來說，影城進駐商圈後能提升房價139萬元。 / These years combination-area of cinemas, malls, recreation and catering are springing up as well as being the eye-catching advertisement of building-contractors. In terms of house-buyers, the correlation between cinemas and the neighborhood housing price is one of the conditions they care. Nevertheless, with the variety of the cineplex-cinema currently and mostly locate in business districts and department stores, our study make a cross analysis between cineplex, cinema, business districts with residential price for their correlation. With cluster analysis to control the heterogeneous samples and to evaluate the price-effect of cineplex-cinemas under the homogeneous housing. Furthermore to multiple regression analyze cinema stationed-in commercial-district and their neighborhood housing price. Our study confirmed the cineplex-cinemas are influential to the Taipei City surrounding-area residential price. For the ten million house, averagely the positive-impact is between four hundred ten thousand to 3.1 million. Negative-impact is between three hundred thousand to 2.71 million. Besides, the housing price different positive effect is higher than the negative one. Further to verify type-mode cinemas: the price difference in neighborhood-area is four hundred thousand with franchise management type-mode. However, there is no price difference with complex and single-service type-mode. Housing-buyer can take the result as consideration during purchasing houses as well as real estate developers and salesman in pricing strategy and advertisement principles of pre-sale houses. Additionally, with the change of consumption patterns, consumers get use to watch movies together with other recreation. Thus commercial-districts combining with department stores and cinemas are more favored. The cinema stationed-in and commercial-district development are complemented each other, therefore to affect the neighborhood housing price in New Taipei City. For the ten million house, averagely, the positive price-different effect is one million and three hundred nighty thousand after the cinema stationed-in.

The housing market segmentation could provide a reference for more targeted policymaking and investment strategies. Although there have been many studies, there are no consistent submarkets delineating methods because of a lack of theoretical support and subjective evaluation. In this paper, two market segmentation methods are introduced. The continuous spatial segmentation divides properties into submarkets according to their coordinates, while the discontinuous spatial segmentation creates submarkets according to the variable having the most significant impact on the price index, which is the construction year of properties. Two valuation methods, the hedonic price model and the XGBoost regression model, are applied to evaluate the overall Stockholm housing markets and the created. The results proved that both market segmentation methods could improve the valuation prediction accuracy compared to the valuation under the overall Stockholm housing market. The non-spatial continuous market segmentation approach delivers more improvement in valuation accuracy but also has greater volatility. As for the two valuation models, no single valuation method can be absolutely advantageous in any market context. / Segmenteringen av bostadsmarknaden skulle kunna utgöra en referens för mer målinriktade politiska beslut och investeringsstrategier. Även om det har gjorts många studier finns det inga konsekventa metoder för att avgränsa delmarknader på grund av brist på teoretiskt stöd och subjektiv utvärdering. I detta dokument presenteras två metoder för marknadssegmentering. Den kontinuerliga rumsliga segmenteringen delar in fastigheter i delmarknader utifrån deras koordinater, medan den diskontinuerliga rumsliga segmenteringen skapar delmarknader utifrån den variabel som har störst inverkan på prisindexet, vilket är fastigheternas byggnadsår. Två värderingsmetoder, den hedoniska prismodellen och XGBoost-regressionsmodellen, används för att utvärdera Stockholms bostadsmarknad och den skapade marknaden. Resultaten visade att båda marknadssegmenteringsmetoderna kunde förbättra värderingens prediktionsnoggrannhet jämfört med värderingen under den övergripande bostadsmarknaden i Stockholm. Den icke-rumsliga kontinuerliga marknadssegmenteringsmetoden ger större förbättringar i värderingsnoggrannheten men har också större volatilitet. Vad gäller de två värderingsmodellerna kan ingen enskild värderingsmetod vara helt fördelaktig i något marknadssammanhang.

The construction and real estate industry accounts for a large share of the global energy consumption. Thus, an onerous responsibility is resting on the shoulders of the property owners and developers to alleviate the emissions derived from the real estate sector. With the escalating necessity for undertaking measures aimed at averting additional negative impact on the climate, a rising level of engagement from governmental as well as private stakeholders emerges. As a consequence of the negative development in the sector, and with tenants willing to pay rentalpremiums to be seated in premises in buildings certified as green, the issuance of green certifications have surged. The purpose of this study is to examine whether the distance from the city center has an impact on the green sales premiums for commercial office properties on the Swedish property market, to be able to compose more accurate calculations of green investments. This is investigated ona national level, as well as for cities exceeding 200,000 inhabitants. The study is a quantitative study which uses hedonic regression models to answer the research questions. The data used is collected through a transaction database provided by Cushman & Wakefield Sweden, with additional data from Sweden Green Building Council and Statistics Sweden, among others. The results conclude an incremental green premium of 14.80% at the 1 km mark from the city center for the country level data set, and 31.92% for the larger cities, at the 10%, respective 1% level of significance. However, some coefficients are inconsistent and the author highlights potential deficiencies within the course of action. / Bygg- och fastighetssektorn står idag för en stor del av den globala energiförbrukningen. Därmed vilar ett tungt ansvar på fastighetsutvecklare, förvaltare och andra aktörer i branschen för att minska utsläppen från fastighetssektorn. Med det ökande behovet av att vidta åtgärder för att undvika ytterligare negativ påverkan på klimatet, har även engagemanget från både offentliga och privata aktörer tilltagit. Som en konsekvens av den negativa utvecklingen inom sektorn och att hyresgäster är villiga att betala en premie för att husera i byggnader som är miljöcertifierade, har utfärdandet av gröna certifikat ökat markant. Syftet med denna studie är att undersöka om avståndet från centrum har en inverkan på den gröna försäljningspremien för kommersiella kontorsfastigheter på den svenska fastighetsmarknaden, för att kunna göra mer exakta investeringskalkyler gällande investeringar i miljöcertifierade fastigheter. Detta undersöks på dels nationell nivå, och dels för städer med över 200 000 invånare. Studien är en kvantitativ studie som använder hedoniska regressionsmodeller för att besvara forskningsfrågorna. Dataseten komponeras med hjälp av en transaktionsdatabas som tillhandahålls av Cushman & Wakefield Sweden, tillsammans med ytterligare data från bl.a. Sweden Green Building Council och Statistiska centralbyrån. Resultatet indikerar en ökande grön premie om 14,80% vid 1 km från stadens centrum för datasetet på nationell nivå, och 31,92% för de större städerna, vid 10% respektive 1% signifikansnivå. Vissa koefficienter är dock inkonsekventa och författaren lyfter fram potentiella brister i handlingsförloppet.

國內近年來有許多文獻在進行特徵價格模型預測時，避免樣本中存在異常點會造成模型估計值產生偏差，會使用統計軟體進行異常點檢測，但皆是直接將檢測出的異常點刪除，未加以著墨探究這些異常點的特徵結構、成因及特色等。因此，本研究透過統計檢定方法，探討刪除異常點前後整體樣本的特徵結構變化，並以個別估價觀點加以探討住宅交易樣本異常點的成因與特色，藉此歸納出實價登錄資料未揭露的重要特徵，以及迴歸模型搜尋疑似申報不實案件之可行性。 透過敘述統計及樣本結構差異檢定結果發現，異常樣本的離散程度相對原始樣本與正常樣本較大，且經過刪除異常點的正常樣本特徵結構差異程度縮小；異常點的形成可能受到區位變數無法反映實際情況及樣本群聚程度影響，也可能因模型未納入某些重要的特徵變數，而使隱含該變數的樣本被判斷為異常點；異常樣本與正常樣本的成交總價、土地坪數、建物坪數、總樓層、所在樓層及屋齡等變數平均數、變異數及中位數有顯著差異。 藉由個案分析結果歸納，可能因異常個案的住宅屬性存在整幢大樓住商混合使用、特殊鄰居、附屬建物占比過高、高總價豪宅產品、都更效益、增建效益、裝潢效益、約定專用空間效益、樓層高度挑高、獨特視野景觀或特殊區位條件；外部環境存在鄰近嫌惡設施或迎毗設施；交易情況存在買方身分特殊之影響，但受限於實價登錄未要求登載並揭露這些特徵，故模型未考量這些因素對價格的影響，使得模型可能將隱含這些特徵的樣本判斷為異常點，並進而影響模型預測結果。另外也發現，實價登錄資料存在登載錯誤及價格申報不實的情況，且可能被模型判斷為異常點。 / Many literatures use statistics-way to detect outliers in preventing any extreme deviation in hedonic price model prediction. Nevertheless, deleting the outliers instead of investigation into the structures, causes and features. Hence, this thesis studies the feature structures variation of the sample before and after deleting the outliers and with the valuations by appraisers’ perspective to inquire into the factors and features of the outliers in residential transactions. Thereby to summarize the significant features that are not disclosed by real price registration and feasibility in searching the possible false declaration of price by regression. Through descriptive statistics and sample structural difference parametric and nonparametric test shows the discreteness level of singular (outliers only) samples is greater than the primary (outliers including) and normal (outliers deleting) samples and the feature structure variation lessened after deleting the outliers in normal samples. The formation of outliers may be influenced by location variable not able to reflect actual circumstances and level of clustering in samples. Maybe some significant variables are not subsumed into the model, which leads to the judgement of samples with this variable to be outliers. The mean, variance and median in total traded price, land size, building size, total floors, exact floor and house age of singular samples are notably different with normal ones. With the analysis of cases, the possible reasons may be residential and commercial mixed-use in building, peculiar neighbors, high proportion of accessory building, luxury houses, urban renewal benefits, building addition benefits, interior decoration benefits, agreed space benefits, high-ceiling benefits, unique view or location, YIMBY and NIMBY property in environment and special relationship between the buyer and seller. Nevertheless, due to the nondisclosure of these features in real price registration that the model does not take these into consideration. That leads to the judgement of samples with these features as outliers and affects the model prediction. Also the registration error and false declaration in price may also be judged as outliers.