Purpose: The purpose of this paper is to identify and analyze crucial facilities management (FM) service dimensions that affect customer satisfaction with regards to the shopping mall sector, and provide useful implications for FM companies.Design/methodology/approach: The study is based on a five‐year longitudinal customer satisfaction survey conducted in five selected shopping malls in Hong Kong. The authors first use stepwise multiple regression method to estimate the relationship between overall customer satisfaction level and nine specific dimensions of FM service for each year, and then compare regression results of five years to identify crucial dimensions.Findings: The research reveals that: management and maintenance of communal facilities is the most crucial dimension with regard to the overall customer satisfaction; the condition of a washroom is another important dimension for customer satisfaction; communication efficiency and efficacious promotion events are also important for maintaining customer satisfaction.Originality/value: This paper presents a longitudinal quantitative survey regarding shopping mall FM service satisfaction and identifies several critical dimensions affecting customer satisfaction, which deliver useful information for FM managers who intend to fulfil customer expectations.

This study measures performance and efficiency of the Listed Real Estate Companies (LRECs). Three types of Data envelopment analysis (DEA) approaches are employed, which are CCR-DEA, BCC-DEA and Super-Efficiency-DEA models. Based on these DEA approaches, we conduct an empirical analysis on the 94 LRECs in China stock markets according to the 2009 Annual Financial Statements. Registered Capital, Asset Value, Employee Number, and Operation Cost are adopted as the inputs factors, while the output factors are denoted by Revenue and Profit. In general, this empirical research delivers four outcomes: firstly, an integrated assessment system and a ranking of the LRECs are established, which provides useful information for investors who are seeking for indirect exposure in the Chinese real estate market. Secondly, the average Overall Efficiency (OE), Pure Technical Efficiency (PTE) and Scale Efficiency (SE) of the LRECs are 0.78, 0.84 and 0.92 respectively. Thirdly, 69% of the inefficient LRECs are classified as increasing returns to scale and could further increase operating efficiency by scale expansion. Fourthly, the employees slack is prevalent at 18.96% for the inefficient LRECs. Santrauka Straipsnyje įvertinamas nekilnojamojo turto kompanijų, kurių vertybiniai popieriai įtraukti į biržos sąrašus (angl. LRECs), veiklos efektyvumas ir rezultatyvumas. Tam taikomi trys duomenų apgaubties analizės (angl. DEA) metodai, o būtent CCR-DEA, BCC-DEA ir Super-Efficiency-DEA metodai. Pritaikius šiuos duomenų apgaubties analizės metodus ir remiantis 2009 m. metinių finansinių ataskaitų duomenimis, buvo atlikta Kinijos akcijų rinkos 94-ių nekilnojamojo turto kompanijų, kurių vertybiniai popieriai įtraukti į biržos sąrašus, empirinė analizė. Įstatinis kapitalas, nominali aktyvų vertė, darbuotojų skaičius ir eksploatacinės išlaidos laikomi įeigos veiksniais, o išeigos veiksniais imamos pajamos ir pelnas. Šiuo empiriniu tyrimu galima pasiekti keturių rezultatų: pirmiausia, sudaryta integruoto vertinimo sistema ir nustatomi nekilnojamojo turto kompanijų, kurių vertybiniai popieriai įtraukti į biržos sąrašus, reitingai. Reitingų teikiama informacija naudinga investuotojams, siekiantiems netiesioginio dalyvavimo Kinijos nekilnojamojo turto rinkoje. antra, nekilnojamojo turto kompanijų, kurių vertybiniai popieriai įtraukti į biržos sąrašus, vidutiniai dydžiai: bendras efektyvumas (angl. OE), grynasis techninis efektyvumas (angl. PTE) ir masto efektyvumas (angl. SE), atitinkamai sudaro 0,78, 0,84 ir 0,92. Trečia, 69 proc. neefektyvių nekilnojamojo turto kompanijų, kurių vertybiniai popieriai įtraukti į biržos sąrašus, priskirtos prie augančio pelno kompanijų, kurios gali kelti veiklos efektyvumą plėsdamos savo veiklą. Ketvirta, 18,96 proc. neefektyvių nekilnojamojo turto kompanių, kurių vertybiniai popieriai įtraukti į biržos sąrašus, būdinga darbuotojų neveiksnumas.

This article investigates the dynamic conditional correlation between price and rental among four types of real estate markets, namely housing, retail, office and factory. In the case of Hong Kong (from January 1993 to December 2008), the Dynamic Conditional Correlation Multivariate Stochastic Volatility (DCC–MSV) model and Bayesian Markov Chain Monte Carlo (MCMC) are employed to capture the volatility and time varying correlation of property price and rental in each market. The empirical results demonstrate that the volatilities of price are significantly larger than those of rental for each type of real estate markets throughout the whole sample period. Besides, the findings indicate that the correlations between price and rental are time-varying, and the average correlation in the housing market is much larger than those in the other three markets. Also, the conditional correlations in the factory market are the most volatile, while those in the office market are relatively stable. Additionally, two particularly volatile periods have been identified in and around 1997 and 2003.