Digitization of Property Operation

White papers on the Digitization of Physical Property Data and Property Operation.

How-To Guides for property owners transitioning physical property detail into machine readable data stored in the cloud.

White papers on the Digitization of Property Operation and Physical Property Data.

How-To Guides for property owners transitioning physical property detail
into machine readable data stored in the cloud.

Digitization of Property, Digitization of Physical Property, Digitization of Property Operation, Deep Data Introduction, Deep Data Benefits, Best Storage Options for Property Data, AI Data in Real Estate, AI Data Library Intro, Digital Data Benefits for NNN Owners, Autonomy in Property Operations, Autonomy in Property Management, Insurance Document Autonomy, PIM Directory Structures, Backend Property Expense, CaPSCi Intro, Construction Project Supply Chain Infrastructures™ (CaPSCi), How Digitized Property Operation Works

Insurance Document Autonomy

It’s one of the most time-consuming tasks involving four independent stakeholders that contribute to determine and process requirements manually. And, one of the easiest workflows to automate when shared interests share similar industry strategies and objectives.

These protections are tied to specific build and service related projects on a property. They’re required when executing a contract for services and expire upon completion or other predetermined date and may renew annually on extended SLA’s.

The manual process usually goes smoothly using minimum resources averaging 3 to 5 man-hours in total. However, it can require 16 man-hours across three stakeholders to process documents.

In both cases, the digitization of property data and its operation introduces autonomy reducing insurance deliverables to minutes at pennies on the dollar.

SLACi digital IAM solutions for real estate industries allow user technologies to process requests without human intervention. Stakeholder detail, AI instructions and user apps operate in the background to authenticate exchange of information between the diverse technologies of users and data resources distributed throughout industry.

How Data-Tech Works

The workflow of project managers generally define who operates within a CaPSCi network but the interoperability of data may transcend several layers of user technology in preparing deliverables. SLACi establishes an agnostic security framework around project stakeholders to authenticate and process digital requests through their unique user apps.

Real estate professionals and service providers follow predictable paths between concept and bid and execution of operational requirements. Each stage builds on a network of service providers involved in a specific task or project.

In most cases, a signed contract between management and a contractor is associated with an exchange of insurance documents.

Future of Documentation

Digitally, COIs can be executed in a few bytes as a verification process replacing a mountain of print to paper computer files. References to additional insures will require a few more bytes. Interoperability between industry segments will continue to simplify analog processes between fragmented interests.

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Autonomy in Property Management

On each property managed, the typical manager oversees dozens of services required every year. Notwithstanding, unscheduled service requirements that emerge dynamically as events around ownership, operation and tenants unfold.

Technology remains inefficient here so the expense is based on time and manpower with initiation costs starting around $500 per service. The expense to manage execution is linear to the complexity of operations.

Organizing simple access to a property where tenants hire security might involve four independent organizations. It’s not unusual for the collective stakeholders to burn through $1,400 in manpower on scheduling alone. Data technology will reduce this expense to pennies.

Managers must deal with large volumes of information, manually performing research, processing details and scheduling services for everything from lightbulb maintenance in parking lots to waterproofing.

Locating information, coordination and scheduling services are time consuming processes performed by diverse stakeholders who need distinct details. Managers are the logical go to source but impacts their workload and productivity. And current methods inevitably repeat with any change in personnel throughout the value chain.

Identifying property specific detail continues to require manual review of documentation that is far from consistent between assets under management. Artificial intelligence can assist in searching computer files but adds expense, a technology step and user process.

Air-gapped computer files must be searched repeatedly by every user, platform or technology of different stakeholders that need information. Deep data on a property is mostly static detail but require periodic updates and uploads. Today, many building systems produce data dynamically.

Capturing equipment data in the cloud eliminates the need for service providers to download the details onsite. Management, contractors and tenants will recover valuable manpower, reduce local traffic and tenant disruption.

Converting computer files that detail property assets, structures and grounds to simple data pairs minimize storage cost and improves its analytic value. Securing deep data in the cloud expands accessibility, transparency, portability and its utility. This allows service provider AI to enhance user and process efficiency through technology built for their fields of expertise.

These data libraries are central to autonomous and transparent exchange of data constants between stakeholders and organizations via technology.

Data Foundations

Cloud positioned data establishes a foundation for autonomy between independent data libraries, technology platforms and isolated ecosystems. The digital transformation of property is to complete the digitization of industry where property, service providers and vendors recognize and manage their data as an AI Data Library.

This information is agnostic to technology and details how a company’s AI implements autonomy and data exchange between data centric platforms. These independent libraries provide a central location for everything from detail and instructions to protocols for data exchange internally and through external communications.

The following are some examples of how AI Data Libraries will be used in practical applications of management autonomy.

Budget Forecasting

The property management library allow tech used by managers to initiate data exchange in the background through normal user processes. Autonomy transforms routine manual tasks to achieve consistent time saving results.

Budget forecasting and updating service provider rates for next year is a great example. This remains a manual task that both managers and contractors repeat annually for multiple service providers and clients respectively.

The vendors personalized AI data library offer a secure reliable source and single point of entry to update annual rate data. The client’s (management) AI data library provides a central point of access for the managers technology to obtain applicable current and house or upcoming rate changes.

Interoperable technologies authenticate the service provider-client relationship and autonomously exchange data without additional user steps, technology or accounts. The data request is initiated within the budget forecasting process and cascades throughout the service provider ecosystem.

Management technology will query service provider data as necessary. If a future rate is not available, the service provider technology will advise the admin of a pending request and process any anticipated change.

Integrated objectives initiate autonomy between diverse technologies operating on independent data to achieve common goals.

Agentic AI Scheduling

Agentic AI scheduling agents will be the backbone of user applications in many industry sectors and segments. Schedule autonomy will be the holy grail that makes us feel like we’re living in the future. Personal technologies, company data and agentic AI will be capable of handling 85% of all scheduling activity within five years of industry embracing AI data and digital exchange.

This is one area where property managers act as liaison for the entire property value chain and service provider ecosystems. Its an environment where any stakeholder can initiate the necessity to coordinate property access or deliverables involving multiple interests. And, it’s one of the most time consuming task in the industry.

Scheduling AI will reduce “tentative” to a data queue of matching variables returned in seconds. Generally and on the surface of exchange between firms, users will be able to select or introduce a preferred date-time. But anyone can accept matching defaults and automatically add items to their mobile and desktop calendar’s.

Below the surface, AI scheduling agents will be used to dispatch personnel and arrange supply chain deliverables. Integrated into contractor project management technology autonomy will schedule where personnel need to be in real time. A combination of platforms and mobile apps will use to analyze and match personnel to field work.

Scheduling agents will connect client and tenant maintenance requests to service providers and personnel in the field. A roofer will repurpose installation activities to leak repairs or reroute HVAC personnel when weather precipitates a change.

Daily schedules will be automatically updated in both our mobile and desktop devices with real time notification for field personnel and activities.

COI Processing

Request for COI and AIE documents is a standard practice throughout the industry. PropTech attempts to process these request autonomously but autonomy stops at the requesting authority (technology) of its user.

The result has been vendors spending up to 16 hours of disruptive manpower to process a single COI or AIE request through PropTech. It may be one of the easiest workflows to automate but its also one of the most complicated data flows between diverse interests and stakeholders.

Real estate embraces various types and standards for insurance including deviations, special instances and exceptions to requirements but relies on variations of boilerplate terms developed over time. Terms that get handed down from individual interest in finance, owners and developers to build, management, service providers and supply chains.

Boilerplates that served their purpose in the analog universe will eventually be replaced by dynamic data customized to established thresholds. Data libraries throughout the value chain will describe and reconcile insurance thresholds on an individual basis. SLACi digital identities provide a foundation for the anonymous technologies of these trusted users to communicate in the background.

The managers platform will process requests on-demand by initiating a vendor query which in turn exchanges data with its insurance agents’ technology to fulfill deliverables. The independence of AI data simplifies the same process in AEC, build, general contractors and others who provide construction and project management.

Information to process a COI or AIE will reside inside the personal AI data libraries of property and organizations throughout the industry. Today, user technologies can collaborate to process insurance documents on-demand but shared industry objectives will transition these analog deliverables to digital bytes within ten years.
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Conclusion

Digitizing property operation is a community effort towards industry efficiency. A mutual objective in recognizing the manual process in terms of digital transparency and exchange of data between property, independent service providers, organizations and supply chains to achieve common goals.

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Autonomy in Property Operation

The necessity of management is underlined by their personal touch, relationships, field experience and insider knowledge on local markets. Technology adds another dynamic to these skills.

A data centric approach defines managers as an administrator who oversees data access for assets under management. But don’t over think data access management, this is one area where technology creates autonomy in workflows to improve user efficiency through effective objectives.

Real estate spans eight unique industry sectors, seven of them lead directly back to property and asset management. Data and a combination of stakeholder technologies will augment workflow integration between these diverse professionals who contribute to deliverables.

Challenges fragmented industries face may well be the foundation for a collective Real Estate Intelligence to emerge thru an interactive exchange of data between independent stakeholder technologies, said Anthony de Kerf, CEO of SLACi Inc.

The combined private data of assets, industry and stakeholders creates a foundation to recover manhours in management and augment declining workforces in build, supply chains and contractor services.

Public vs. Private Data

Publicly, the reputation of an asset is dependent on the accuracy of details accessible to consumer facing technologies like MLS. Human error is not unusual but applications injecting public data from sources that produce AI hallucinations is unacceptable.

Private property data positioned for AI replaces public information with a single source of reliable details to create value, improve workflow and eliminate error. Reliable information is necessary to help external agents accurately position property.

Property management companies and personnel highlight a unique value proposition managers offer the owner / investor. A firms operating model (BOM) as AI data will continue to play a critical role in positioning technology to enhance the human side of this digital transition.

Property operation is a moving target where variables change with the character of assets constantly in a state of flux adapting to age, markets and performance.

The concept of fully autonomous operation is challenged by many levels of decision making that coalesce at management before merging with owners. However, autonomous exchange of data between stakeholders will make it easier to analyze the findings and opinions of subject matter experts!

Note the use of “between users” as its unlikely property will ever operate entirely in an autonomous manner. However, “autonomous workflows” will improve efficiency to lower cost and recover valuable manpower for management and service providers.

Notwithstanding property management offers both a unique and boutique service. Boutique being on the side of whatever a property needs at any given point during its serviceable life. The most effective technologies will continue to be dependent on management as the conduit between the property, its data and service providers.

It required fifteen years of research to identify the core components our industry needs to complete the digital transformation of real estate. The most strategic and cost effective elements include deployment of private data on property and management firms as enterprise grade AI Data Libraries.

SLACi pioneered the CaPSCi Architecture to help owners capture and store build data digitally with secure access and exchange by service provider ecosystems.

The only constant in the business of commercial property is the building and its grounds. That information is central to stakeholders on both the front and backend of operations.

Property doesn’t operate under the umbrella of a single organization. Real estate revolves around highly diverse ecosystems of independent stakeholders fragmented by subject matter and expertise dependent on unique datasets distributed throughout the industry.

The common denominator between build, operation and service providers is in the unique dataset of each property. Engaging a single service creates cascading demand for data that radiates through various industry segments and sectors. A collection of deliverables create additional data to be captured for utility and retained for immediate decisions and future analytics.

Owners, management teams and service providers share common and equal dependencies on property details in both commercial and residential markets. An independent data centric technology approach allow diverse stakeholders to adopt solutions specific to their field and contributions to an end deliverable on the property or specific project.

Autonomy

Autonomous workflows between users in different fields and organizations requires a minimum of three data libraries. The primary library is the property data with an accessible CDE in the cloud. A public hard drive where build and operational data can be captured and stored for continuous utility and routine updates.

Management is the second most important data library in real estate. They offer critical details required to guide technology on operating decisions and manage data access. Service provider libraries provide information essential to real time communications, user security, trade efficiency and digital deliverables.

Data is foundational to workflow autonomy. Property and service provider AI data libraries provide essential detail technologies need to integrate workflows in such a diverse ecosystem of property assets, interests, oversight and types of data. The market price to build an AI data library for property depends on the asset and available data. Independent service providers may spend a few hundred to a few thousand or more. In both cases, the total cost will be determined by sophistication of the data library as built and expensed over time.

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