This research is intended to assess Blueprint Residential Property Management Company's current organizational structure, business processes, system infrastructures, policies, leadership skills, and employees to determine areas of change and realignment to move the organization to a leading-edge service company. The research is also intended to factor in opportunities that need to be tapped into due to the impact of the global COVID-19 pandemic and recent Bill-20 legislation passed by the Government of Alberta.
Union: Sustainable Development Co-operative (Union Co-operative) seeks to democratize city-building by empowering its members to collectively buy, upgrade, and manage commercial and residential properties to improve the environmental, social, and economic health of Waterloo Region. This project will support the evolution of the Co-operative’s model, the development of affordable housing for refugees, and create templates that can be implemented by other communities seeking to establish affordable rents and community control of property.
The BC Real Estate Association aims to develop the localized flood vulnerability index (LOFVI). The intention is to increase awareness of new floodplain maps among governments, realtors and consumers through the final report and assess the impact of senior government funding programs. The first step will be to update and prepare an inventory of floodplain maps across the province of BC, which will provide important information that communities need for land use decisions alongside helping property buyers make informed decisions.
Start-ups face several challenges throughout in their attempts to position themselves as goods or services providers. In fact, they can fail at different growth stages. Particularly, they terminate operations before their value propositions advance to solidified business models.
The current pandemic is changing how people work, and creating a new way to think about home. From setting up a kitchen office, to being able to drop a lengthy commute, people across Canada are interacting with their living space in new ways. First time homebuyers (FTHB) have a unique opportunity to re-evaluate what is important to them when house-hunting, but the ways in which the real estate industry communicate with prospective buyers is slow to change. This project asks Millennial FTHB directly about what they look for in a first home, and how they find that information.
Thermal energy storage (TES) systems are suitable to bridge the existing mismatch between demand and supply. In current study, borehole thermal energy storage (BTES) is selected due to its advantages. The performance of the BTES can be affected by several factors in different patterns including design, operation, and geological and material properties.
Individuals with mood disorders suffer with a significant burden. Beside experiencing mood symptoms, they also have cognitive and functioning impairments, are at greater risk of suicide and hospitalization, and have a poorer quality of life. Traditional psychiatric assessments are subjective and not able to capture events in real-time. Smartphone assessments and digital phenotyping – the interaction between user and smartphone captured in real-time – can provide a more robust, objective, and extensive view of what is going on with mood disorder subjects.
The goal of the project is to evaluate the effectiveness of video as medium of delivery and consumption of large quantities of digital information. The team will create an automated way to generate videos for properties and rental information so as to reduce costs on manual video creation and build user profiles based on smart questions to present tailored information based on users’ needs in a video format. Through researching, implementing technologies, designing analytics tools and developing a prototype we hope to evaluate the effectiveness of dynamic video information delivery.
As Covid-19 Pandemic shows that we are surrounded by global unforeseen risks. Housing and housing financing are significant parts of people’s life and global crises may always put people’s right to housing in danger. For this very reason, it is a necessity to look for alternative financing models for housing as affordability would decrease the potential risks that people may face in the cases of global crises. Therefore, this project aims at working on alternative financing models in order to find the most proper and the best models for creating affordable housing systems.
Finding the right real estate property that grows in value over the next few years is of paramount importance. For doing so, one of the most important factors is to estimate the current value of the property as well as its future value. The goal of this project is to build a data and domain-driven model using machine learning that uses previous real estate data to estimate the value of property and suggest the right property in the right neighborhood for investment. We will build an end-to-end framework to collect and preprocess the data and then predict the value of a property.