Ancoris | Google Maps For Housing SaaS application

Reduce rent arrears and increase revenues

Example: Directors visualise all rent arrears with any agreement in place, court action or universal credit and spot trends and patterns in the data. They layer demographic data for greater context. A course of action is agreed, such as running campaigns in hotspots and working with tenants to improve their situation.

Visualise information to allow proactive action to prevent arrears
Proactively plan and visit tenants at risk of rent arrears

Improve customer service and satisfaction

Example: A family with two young children is looking for a home within walking distance of a primary school and close to a play area. The ability to layer the information on available homes with additional local data means this can be visualised easily within the application. There is no need to search multiple sources.

Match tenants’ personal needs to local areas.
Provide prospective tenants with relevant information so they can make quicker and better informed decisions.

Ensure gas safety checks are valid and SLA’s met

Example: Estates officer filters gas safety checks on a map to identify those that are invalid or about to lapse. He accesses this information in the field and visit properties nearby there and then.

Proactively visit tenants when in the close vicinity and gain timely access
Identify location patterns and trends and take proactive action to avoid lapsed checks

Improve staff productivity

Example: Estate officers accesses live information and filters rent arrears for their assigned properties on a map. This displays their schedule of visits and automatically suggests the optimum route.

Assign optimum geographic areas and improve patch working
Improve travel planning and increase the daily tenant visits that officers can make
Access all housing information in one place on a map and improve time management.

Get repairs done in time

Example: Jobs are allocated to maintenance and repair crews based on the job location, the crew operating area or even live location. Information on areas that are underperforming are analysed so that action, such as appointing additional crews to an area, may be taken.

Allocate maintenance crews based on location and optimise job routing with live traffic information and estimated times of arrival.
Identify location patterns and trends.

Reduce void housing

Example: Use heat and clustered maps to visualise all void properties, past and present, identifying a trend in the data. Run the analysis against demographic data to give it greater context and highlight an area of high voids. This will help plan for future housing stock, potentially selling properties in that area and planning new builds in areas with higher demand.

Identify single or cluster of voids and take remedial action.
Fill void properties quickly with appropriate tenants.

Visualise all your property information on Google Maps

Making it easy for the staff of housing associations to access their data from anywhere and visualise it on a familiar map

Achieve operational cost savings