217 resultados para rental


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Mode of access: Internet.

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Translation of: La Gerusalemme liberata.

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Mode of access: Internet.

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Canoe rental dock on the Huron River, Ann Arbor, Michigan. Copyprint of original

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Mode of access: Internet.

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Customer Base Analysis is perhaps the first stage of analysis in customer value, aiming to predict purchase frequency and customer lifecycle. An important part of the customer purchase frequency and its retention has to do with the service upgrade. Many models have tried to predict purchase frequency as well as upgrading. The comparison of these models seems important to provide academics with a picture of the current situation. The purpose of this research is to evaluate how models can predict service upgrade among a customer database of an online DVD rental company and suggest an alternative based on data mining techniques and data on historical transactions.

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Around 80% of the 63 million people in the UK live in urban areas where demand for affordable housing is highest. Supply of new dwellings is a long way short of demand and with an average annual replacement rate of 0.5% more than 80% of the existing residential housing stock will still be in use by 2050. A high proportion of owner-occupiers, a weak private rental sector and lack of sustainable financing models render England’s housing market one of the least responsive in the developed world. As an exploratory research the purpose of this paper is to examine the provision of social housing in the United Kingdom with a particular focus on England, and to set out implications for housing associations delivering sustainable community development. The paper is based on an analysis of historical data series (Census data), current macro-economic data and population projections to 2033. The paper identifies a chronic undersupply of affordable housing in England which is likely to be exacerbated by demographic development, changes in household composition and reduced availability of finance to develop new homes. Based on the housing market trends analysed in this paper opportunities are identified for policy makers to remove barriers to the delivery of new affordable homes and for social housing providers to evolve their business models by taking a wider role in sustainable community development.

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Significant numbers of homes within the UK are at risk of flooding. Although community level flood protection schemes are the first line of defence for mitigating flood risk, not all properties are protectable. Property-Level Flood Protection (PLFP) provides those unprotected homeowners with an approach for protecting their homes from flooding. This study sought to establish why property-level flood protection is needed and secondly assess the extent of take up using Worcester as the study area. An exploratory questionnaire survey was conducted to achieve these objectives. After consultation of available literature it was established that the introduction of PLFP protection provided numerous benefits including limiting the health & psychological effects flooding poses, the direct financial benefits and also the possible influence on gaining flood insurance. Despite the benefits and the recognition given to PLFP by the government it was found that the overall take up of the measures was low, findings which were further backed up by data collected in the study area of Worcester with only 23% of the sample having introduced PLFP measures. Reasoning for the low take up numbers typically included; unawareness of the measures, low risk of flood event, installation costs and inability to introduce due to tenancy. Age was noted as a significant impacting factor in the study area with none of the respondents under 25 suggesting they had “a good amount of knowledge of PLFP measures” even when they claimed their properties to be at risk of flooding. Guidance and support is especially recommended to those who are unable to manage their own flood risk for e.g. social housing/rental tenants.

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Traffic from major hurricane evacuations is known to cause severe gridlocks on evacuation routes. Better prediction of the expected amount of evacuation traffic is needed to improve the decision-making process for the required evacuation routes and possible deployment of special traffic operations, such as contraflow. The objective of this dissertation is to develop prediction models to predict the number of daily trips and the evacuation distance during a hurricane evacuation. ^ Two data sets from the surveys of the evacuees from Hurricanes Katrina and Ivan were used in the models' development. The data sets included detailed information on the evacuees, including their evacuation days, evacuation distance, distance to the hurricane location, and their associated socioeconomic characteristics, including gender, age, race, household size, rental status, income, and education level. ^ Three prediction models were developed. The evacuation trip and rate models were developed using logistic regression. Together, they were used to predict the number of daily trips generated before hurricane landfall. These daily predictions allowed for more detailed planning over the traditional models, which predicted the total number of trips generated from an entire evacuation. A third model developed attempted to predict the evacuation distance using Geographically Weighted Regression (GWR), which was able to account for the spatial variations found among the different evacuation areas, in terms of impacts from the model predictors. All three models were developed using the survey data set from Hurricane Katrina and then evaluated using the survey data set from Hurricane Ivan. ^ All of the models developed provided logical results. The logistic models showed that larger households with people under age six were more likely to evacuate than smaller households. The GWR-based evacuation distance model showed that the household with children under age six, income, and proximity of household to hurricane path, all had an impact on the evacuation distances. While the models were found to provide logical results, it was recognized that they were calibrated and evaluated with relatively limited survey data. The models can be refined with additional data from future hurricane surveys, including additional variables, such as the time of day of the evacuation. ^