875 resultados para mortgage transaction


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This paper investigates whether energy performance ratings, as measured by mandatory Energy Performance Certificates (EPCs), are reflected in the sale prices of residential properties. This is the first large-scale empirical study of this topic in the UK involving approximately 400,000 dwellings in the period from 1995 to 2011. Applying hedonic regression and an augmented repeat sales regression, we find a positive relationship between the energy efficiency rating of a dwelling and the transaction price per square metre. The price effects of superior energy performance tend to be higher for terraced dwellings and flats compared to detached and semi-detached dwellings. The evidence is less clear-cut for house price growth rates but remains supportive of an overall positive association. Overall, the results of this study appear to support the hypothesis that energy efficiency levels are reflected in UK house prices, at least in recent years.

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The Twitter network has been labelled the most commonly used microblogging application around today. With about 500 million estimated registered users as of June, 2012, Twitter has become a credible medium of sentiment/opinion expression. It is also a notable medium for information dissemination; including breaking news on diverse issues since it was launched in 2007. Many organisations, individuals and even government bodies follow activities on the network in order to obtain knowledge on how their audience reacts to tweets that affect them. We can use postings on Twitter (known as tweets) to analyse patterns associated with events by detecting the dynamics of the tweets. A common way of labelling a tweet is by including a number of hashtags that describe its contents. Association Rule Mining can find the likelihood of co-occurrence of hashtags. In this paper, we propose the use of temporal Association Rule Mining to detect rule dynamics, and consequently dynamics of tweets. We coined our methodology Transaction-based Rule Change Mining (TRCM). A number of patterns are identifiable in these rule dynamics including, new rules, emerging rules, unexpected rules and ?dead' rules. Also the linkage between the different types of rule dynamics is investigated experimentally in this paper.

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Shifts in credit supply could have a bearing on house prices e.g. through financial innovations and changes in regulation independently of the existence of a bank lending channel of monetary policy. This paper assesses the responses of US house prices to an exogenous credit supply shock and compares them with the effects from variations in credit supply associated with a bank lending channel. The contribution of the study is twofold. First, innovations in credit supply are identified using a mortgage mix variable, thereby accounting for the market-based financial intermediaries. As a robustness check a survey variable of bank lending standards for mortgage loans is also used. Second, the policy-induced credit supply effect on house prices is disentangled and compared with the effect from an exogenous credit supply shock. It is shown that in the first 3 years credit supply shocks affect house prices exogenously rather than through the bank lending channel. Monetary policy has still a large impact on house prices, even when the bank lending channel is ‘turned off’.

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A Characteristic of a landmark case is that it stands for a proposition of law. In Earl of Aylesford v Morris,1 Lord Selborne held that where there existed an inequality between contracting parties, with weakness on one side and an extortionate advantage taken of that weakness on the other, the contract could not stand unless the party claiming the benefit of the contract could rebut the presumption by establishing that the transaction was ‘fair, just and reasonable’. This chapter examines the historical circumstances behind the formulation of this proposition.

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In financial research, the sign of a trade (or identity of trade aggressor) is not always available in the transaction dataset and it can be estimated using a simple set of rules called the tick test. In this paper we investigate the accuracy of the tick test from an analytical perspective by providing a closed formula for the performance of the prediction algorithm. By analyzing the derived equation, we provide formal arguments for the use of the tick test by proving that it is bounded to perform better than chance (50/50) and that the set of rules from the tick test provides an unbiased estimator of the trade signs. On the empirical side of the research, we compare the values from the analytical formula against the empirical performance of the tick test for fifteen heavily traded stocks in the Brazilian equity market. The results show that the formula is quite realistic in assessing the accuracy of the prediction algorithm in a real data situation.

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The performance of real estate investment markets is difficult to monitor because the constituent assets are heterogeneous, are traded infrequently and do not trade through a central exchange in which prices can be observed. To address this, appraisal based indices have been developed that use the records of owners for whom buildings are regularly re-valued. These indices provide a practical solution to the measurement problem, but have been criticised for understating volatility and not capturing market turning points in a timely manner. This paper evaluates alternative ‘Transaction Linked Indices’ that are estimated using an extension of the hedonic method for index construction and with Investment Property Databank data. The two types of indices are compared over Q4 2001 to Q4 2012 in order to examine whether movements in these indices are consistent. The Transaction Linked Indices show stronger growth and sharper declines than their appraisal based counterparts over the course of the cycle in different European markets and they are typically two to four times more volatile. However, they have some limitations; for instance, only country level indicators can be published in many cases owing to low trading volumes in the period studied.

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Modern transaction cost economics (TCE) thinking has developed into a key intellectual foundation of international business (IB) research, but the Williamsonian version has faced substantial criticism for adopting the behavioral assumption of opportunism. In this paper we assess both the opportunism concept and existing alternatives such as trust within the context of IB research, especially work on multinational enterprise (MNE) governance. Case analyses of nine global MNEs illustrate an alternative to the opportunism assumption that captures more fully the mechanisms underlying failed commitments inside the MNE. As a substitute for the often-criticized assumption of opportunism, we propose the envelope concept of bounded reliability (BRel), an assumption that represents more accurately and more completely the reasons for failed commitments, without invalidating the other critical assumption in conventional TCE (and internalization theory) thinking, namely the widely accepted envelope concept of bounded rationality (BRat). Bounded reliability as an envelope concept includes two main components, within the context of global MNE management: opportunism as intentional deceit, and benevolent preference reversal. The implications for IB research of adopting the bounded reliability concept are far reaching, as this concept may increase the legitimacy of comparative institutional analysis in the social sciences.

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Purpose – Commercial real estate is a highly specific asset: heterogeneous, indivisible and with less information transparency than most other commonly held investment assets. These attributes encourage the use of intermediaries during asset acquisition and disposal. However, there are few attempts to explain the use of different brokerage models (with differing costs) in different markets. This study aims to address this gap. Design/methodology/approach – The study analyses 9,338 real estate transactions in London and New York City from 2001 to 2011. Data are provided by Real Capital Analytics and cover over $450 billion of investments in this period. Brokerage trends in the two cities are compared and probit regressions are used to test whether the decision to transact with broker representation varies with investor or asset characteristics. Findings – Results indicate greater use of brokerage in London, especially by purchasers. This persists when data are disaggregated by sector, time or investor type, pointing to the role of local market culture and institutions in shaping brokerage models and transaction costs. Within each city, the nature of the investors involved seems to be a more significant influence on broker use than the characteristics of the assets being traded. Originality/value – Brokerage costs are the single largest non-tax charge to an investor when trading commercial real estate, yet there is little research in this area. This study examines the role of brokers and provides empirical evidence on factors that influence the use and mode of brokerage in two major investment destinations.

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Nonlocal investors purchase and sell investment property in a distant metropolitan area. In this study, we identify capital value underperformance for nonlocal investors on both sides of the transaction, when they purchase and when they sell. The commercial real estate transactions data include a national sample of office property occurring in more than 100 U.S. markets. Using propensity-score matched sample to control for selection bias, we find that nonlocal investors overpay on the purchase by an estimated 13.8 % and sell at an estimated 7 % discount. These disadvantages relative to local investors expand with the geographic distance separating investor and asset. Nonlocal investors fundamentally overvalue similar assets sold to each other relative to assets transacted between locals, and are less patient as sellers. The positive bias in overpayment is directly tied to office rent differentials between the asset and investor markets.

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Firms outsource through connecting to local and global supply bases and making such connections produces costs of search and evaluation, which are a function of transaction characteristics and firm capabilities. We argue that firms outsource more when those costs are low. Hence, domestic subsidiaries of multinational firms, with low cost access to both local and global supply bases, outsource more than either domestic firms or foreign subsidiaries, as confirmed by evidence from a large data panel. We also propose that among foreign subsidiaries, distance from the home country co-determines search and evaluation costs such that subsidiaries from more distant countries outsource less. This is confirmed for geographic distance, but a positive effect is found for political distance and a mixed effect for cultural distance.

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Research has highlighted the usefulness of the Gilt–Equity Yield Ratio (GEYR) as a predictor of UK stock returns. This paper extends recent studies by endogenising the threshold at which the GEYR switches from being low to being high or vice versa, thus improving the arbitrary nature of the determination of the threshold employed in the extant literature. It is observed that a decision rule for investing in equities or bonds, based on the forecasts from a regime switching model, yields higher average returns with lower variability than a static portfolio containing any combinations of equities and bonds. A closer inspection of the results reveals that the model has power to forecast when investors should steer clear of equities, although the trading profits generated are insufficient to outweigh the associated transaction costs.

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This paper examines the lead–lag relationship between the FTSE 100 index and index futures price employing a number of time series models. Using 10-min observations from June 1996–1997, it is found that lagged changes in the futures price can help to predict changes in the spot price. The best forecasting model is of the error correction type, allowing for the theoretical difference between spot and futures prices according to the cost of carry relationship. This predictive ability is in turn utilised to derive a trading strategy which is tested under real-world conditions to search for systematic profitable trading opportunities. It is revealed that although the model forecasts produce significantly higher returns than a passive benchmark, the model was unable to outperform the benchmark after allowing for transaction costs.

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This paper presents and implements a number of tests for non-linear dependence and a test for chaos using transactions prices on three LIFFE futures contracts: the Short Sterling interest rate contract, the Long Gilt government bond contract, and the FTSE 100 stock index futures contract. While previous studies of high frequency futures market data use only those transactions which involve a price change, we use all of the transaction prices on these contracts whether they involve a price change or not. Our results indicate irrefutable evidence of non-linearity in two of the three contracts, although we find no evidence of a chaotic process in any of the series. We are also able to provide some indications of the effect of the duration of the trading day on the degree of non-linearity of the underlying contract. The trading day for the Long Gilt contract was extended in August 1994, and prior to this date there is no evidence of any structure in the return series. However, after the extension of the trading day we do find evidence of a non-linear return structure.

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This paper analyses the impact of trading costs on the profitability of momentum strategies in the United Kingdom and concludes that losers are more expensive to trade than winners. The observed asymmetry in the costs of trading winners and losers crucially relates to the high cost of selling loser stocks with small size and low trading volume. Since transaction costs severely impact net momentum profits, the paper defines a new low-cost relative-strength strategy by shortlisting from all winner and loser stocks those with the lowest total transaction costs. While the study severely questions the profitability of standard momentum strategies, it concludes that there is still room for momentum-based return enhancement, should asset managers decide to adopt low-cost relative-strength strategies.

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This paper considers the use of Association Rule Mining (ARM) and our proposed Transaction based Rule Change Mining (TRCM) to identify the rule types present in tweet’s hashtags over a specific consecutive period of time and their linkage to real life occurrences. Our novel algorithm was termed TRCM-RTI in reference to Rule Type Identification. We created Time Frame Windows (TFWs) to detect evolvement statuses and calculate the lifespan of hashtags in online tweets. We link RTI to real life events by monitoring and recording rule evolvement patterns in TFWs on the Twitter network.