A model for pricing real estate derivatives with stochastic interest rates

Ciurlia, Pierangelo; Gheno, Andrea

doi:10.1016/j.mcm.2008.12.005

Cited by 21 publications

(5 citation statements)

References 12 publications

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“…Interest rates also are very important for real estate. Ciurlia and Gheno (2009) wrote that real estate derivates market allows participants to manage risk and return expository form to property, without buying or selling directly the underlying asset. Such a market is growing very fast, hence, the need to rely on simple yet effective pricing models is very great.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Impact Factors on the Real Estate Market: Case Slovenia

Golob

Bastič

Pšunder

2012

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The real estate market is very important for every country, not merely because it ensures construction structures and infrastructure necessary for life and work, but also because it has a strong, multiple impact on the development of nation's entire economy. Real estate market trends are strong indicator of trends in the entire economy. Trends on the real estate markets have changed notably in recent years. As early as two years ago, the main characteristics of these real estate markets were domination of demand over offer, quick sales of apartments and a lack of "choosiness" on the part of buyers, all of which was accompanied by rising real estate prices, which has not abated in nearly two decades. Over the past two years, the conditions have changed notably: transactions are few, offer is dominating over demand, buyers are choosy, while apartment prices are falling. In the past, little attention has been paid to the problems we are about to study. Some researches have been conducted; however, none were developed so as to be applicable to the entire territory of Slovenia. For the very facts stated above, we decided to study and present the impact factors on the Slovenian real estate market using a practical example. The answers obtained through a structured survey questionnaire give rise to several questions. One of these questions is also what the primary factors are and how to form the best selection thereof. To determine them, it was our task to monitor the key factors. We obtained some of the information from past research and put them into the time context. Determining the goals and strategies, we elaborated a plan of the system for establishing the factors, and monitored the volatility of the selected indicators. Included into the research were also investors, real estate owners, tenancy right holders, real estate users, administrators, managers, tenants, real estate agencies and companies, design and construction companies, as well as other individuals across Slovenia, with varying durations of work experience and varying education levels. With the research that follows, we wanted to present the factors which most affect the real estate prices on the Slovenian real estate market, resulting in their rise or fall respectively. The role, significance and usage of the assessed parameters on the real estate market are important for efficient operation of construction company. There is a growing interest for modifying and determining the causes of the assessed parameters. Knowledge of conditions on the real estate market is extremely important for efficient and successful operation of an organisation. An analysis of the real estate market and the operation of construction companies represent a starting point for revamping it. The goal of the research is to define the factors that must be taken into account for an analytical forecast of future trends in the fluctuation of real estate prices. We have established that the main group of impact factors is associated with economic growth, interest rates, construction...

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Section: Introductionmentioning

confidence: 99%

Analysis of Impact Factors on the Real Estate Market: Case Slovenia

Golob

Bastič

Pšunder

2012

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“…The spread over LIBOR is highly dependent on the volatility of index returns and on counterparty default risk. The Black‐Scholes risk‐neutral methodology was applied by Ciurlia and Gheno (2008) for pricing real‐estate derivatives such as European and American options on real‐estate assets with a two‐factor model. Syz (2008) gives an overview of the markets and discusses pricing of forwards in the Black‐Scholes framework identifying the deficiencies but not providing a satisfactory solution.…”

Section: Methodological Issues Related To Real Estate Derivativesmentioning

confidence: 99%

Property Derivatives for Managing European Real‐Estate Risk

Fabozzi¹,

Shiller²,

Tunaru³

2009

Euro Fin Management

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Although property markets represent a large proportion of total wealth in developed countries, the real-estate derivatives markets are still lagging behind in volume of trading and liquidity. Over the last few years there has been increased activity in developing derivative instruments that can be utilised by asset managers. In this paper, we discuss the problems encountered when using property derivatives for managing European real-estate risk. We also consider a special class of structured interest rate swaps that have embedded real-estate risk and propose a more efficient way to tailor these swaps.

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“…Cadastral values were determined in 2014 as market values, and also followed lognormal distribution. Such a form of distribution of prices in real estate was pointed out not only by Aitchinson and Brown (the researchers of the University of Cambridge) back in 1963 [1], but also by authors of relatively recent publications [2][3][4]. Rusakov [5; 6] proved that prices formed by sequential comparisons under conditions of perfect competition aim at formation of lognormal distribution of the population.…”

Section: The Modelmentioning

confidence: 99%

Determination of the trading discount based on market data and cadastral value

Laskin¹

2018

Bus. Inform.

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The introduction of the institution of cadastral value in the Russian Federation opens up new opportunities in real estate valuation. In this regard, the new focus for appraisers is statistical analysis of multidimensional empirical distributions that were not previously available, because the real estate market does not have pairwise and multidimensional observations concentrated in unifi ed databases. Data of interest to analysts is usually concentrated in diff erent sources from diff erent owners and pertains to diff erent objects. The goal of combining them can be solved by comparing such data with the data of cadastral records, namely the cadastral number as a unique identifi er of the object. Since the cadastral value corresponds to each cadastral number, it is possible to compare the cadastral value with important indicators such as the market price of the off er, the transaction price, the rental rate, the annual price indices, the capitalization rate, the discount rate, the trading discount and many other indicators, the formation of which involves more than two random variables. The construction of the model involves the principle of following the prices formed by pair comparisons to geometric Brownian motion, and hence the formation of lognormal population. As it turned out, as a result of large-scale cadastral work carried out in the Russian Federation in 2014, the cadastral value is also subject to lognormal distribution of prices (in each class of objects). For the market value (as the most probable price of the transaction under conditions of perfect competition), this leads to functional dependences from the cadastral value of the power type. Similarly, many other indicators will also be subject to dependences in the form of power functions. Obviously, having a function depending from the various indicators of cadastral value across the set of values, you can set the relationship between the various indicators, which was impossible before the introduction of the institution of cadastral value. This article proposes a method of calculating the trading discount when appraising real estate based on analysis of market statistics and databases of cadastral records. An analytical formula of the dependence of trading discounts from the off er price is proposed. The method allows us to set the level of the discount not only for objects included in an advertising database, but also for any object that has undergone cadastral registration.

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A model for pricing real estate derivatives with stochastic interest rates

Cited by 21 publications

References 12 publications

Analysis of Impact Factors on the Real Estate Market: Case Slovenia

Analysis of Impact Factors on the Real Estate Market: Case Slovenia

Property Derivatives for Managing European Real‐Estate Risk

Determination of the trading discount based on market data and cadastral value

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