Hyly.AI Glossary

Let’s say in the month of January at a property or PMC gets leads from different places:

• 50 people filled out a form on an ILS (like Apartments.com).

• 30 people visited the property website and scheduled a tour.

• 20 tours were scheduled by agents (they filled out the form for the person). That means there were 100 Created Contacts in total in January.

Let’s say in the month of January at a property:

• 50 leads scheduled their first tour using the Hyly.AI scheduler

• 30 leads walked in for a tour

• 20 tours were scheduled by leads or agents using a form

• But 20 of these had already scheduled a tour in a previous month.

So, the total number of first tour Scheduled Contacts in January = 80

Let’s say in the month of January at a property:

• 50 tours were marked as completed by agents in Hyly.AI.

• 30 tours were marked as completed by agents in the PMS.

• All 50 leads had never toured the property before, making this their first tour.

• 5 leads who completed their first tour in December toured again in January.

• Since these 5 leads were already counted as Toured Contacts in December, they are excluded from the January count to avoid double-counting.

Therefore, the total number of Toured Contacts for January is 80, as it includes only first-time tours in January, excluding any leads who had already toured in the previous month.

In the month of January at a property:

• 20 applications were submitted by agents in the PMS.

• These were the first applications submitted by these leads in January.

• 5 leads applied in December and applied again in January. These leads are excluded from the January count, as they were already considered Applied Contacts in December.

Therefore, the total number of Applied Contacts for January is 20, excluding those leads who had applied in the previous month.

Let’s say in the month of January at a property:

• 10 leases were registered by agents in the PMS.

• These were the first leases for these leads.

• Out of the 10 leases:

◦ 8 had move-in dates in January.

◦ 2 had move-in dates in February. Leased Contacts Calculation (based on move-in):

• Since the property has selected move-in as the point of lease completion:

◦ Leased Contacts for January are counted based on those who moved in during January.

• The 8 leads who moved in during January are considered Leased Contacts for January.

Leased Contacts for January = 8

Leased Contacts Calculation (based on lease signing):

• If the property had selected lease signing as the point of lease completion, all 10 leases would have been counted as Leased Contacts for January, since all 10 leases were signed within the month..

 Let’s say in January at a property:

• 50 leads came through an Internet Listing Service (ILS)

• 30 leads visited the property website and scheduled a tour

• 20 tour scheduler forms were filled out by agents

• All of these leads were influenced by at least one source Therefore, the Created Prospects in January = 100 

Let’s say in January at a property:

• 40 leads scheduled their first tour using the Hyly.AI scheduler.

• 30 leads walked in for a tour.

• 20 tours were scheduled by leads or agents using a form.

• All of these leads were influenced by at least one source after their contact was created and before the tour was scheduled Therefore, the Scheduled Prospects for January = 90 

 Let’s say in January at a property:

• 50 tours were marked as complete by agents in Hyly.AI.

◦ Of these, 45 leads were influenced by a source before the tour—such as a Hyly autoresponder.

◦ The remaining 5 leads were not influenced by any source, so they do not count toward Toured Prospects as they weren’t actively engaged.

• 30 tours were marked as complete by agents in the PMS.

◦ Of these, 25 leads interacted with sources like ILS listings or Google search before touring.

◦ The remaining 5 leads were not influenced by any source, so they do not count toward Toured Prospects either.

• All of these were first-time tours for the leads.

Toured Prospects Calculation:

• Toured Prospects for January (Hyly.AI): 45 leads who were influenced by a source before their tour.

• Toured Prospects for January (PMS):

25 leads who were influenced by a source before their tour.

Total Toured Prospects for January = 45 (Hyly.AI) + 25 (PMS) = 70 

 Let’s say in January at a property:

• 20 applications were submitted by agents in the PMS.

• Of those, 10 leads searched for the property on Google before applying.

• Another 8 leads visited an ILS and engaged with a marketing alert email before applying.

• All of these were the first applications submitted by these leads Therefore, Applied Prospects for January = 10 + 8 = 18 

 For a property or PMC in the month of January:

• 10 leases were registered by agents in the PMS with move-in dates in January.

• Of these, 5 prospects searched for the property on Google and an ILS before moving in.

• 3 engaged with a marketing email during the same pre-move-in period.

• These leases represent the first lease for each of these leads (i.e., they are not repeat or renewal leases).

Based on this engagement activity, the Number of Leased = 8, as 8 out of 10 move-ins had meaningful marketing interactions before move-in. 

Let’s say in January at a property:

• 50 leads scheduled their tour using the Hyly.AI scheduler

• 30 leads walked in for a tour

• 20 tours were scheduled by leads or agents using a scheduler form

Therefore, Total Tour Schedules for January = 50 + 30 + 20 = 100 

For a property or PMC in January:

• 50 leads scheduled their first tour using the Hyly.AI scheduler.

• 30 leads walked in for a tour without prior scheduling.

• 20 tours were scheduled manually by leads or agents via the tour scheduler form.

• This results in 100 scheduled tours in total. Of these 100 scheduled tours:

• 10 tours were marked as completed in January by agents in Hyly.AI.

• 15 tours were marked as completed in January by agents in the PMS.

Therefore, the Number of Total Toured is 25, representing the completed tours that were scheduled within the month, regardless of how the tours were scheduled (via Hyly.AI, walk-ins, or manually). 

Let’s say in January:

• 10 leads were created during the month.

• Out of those 10 leads, 4 leads completed a tour.

◦ 2 leads took their first tour in January.

◦ 2 leads took their first tour in February.

To calculate the overall Lead to Toured Rate:

• We consider all the leads created in January (which is 10).

• Out of those 10 leads, 4 leads completed their first tour (even if it was in a later month).

So, the overall Lead to Toured Rate is:

(Leads who toured ÷ Total leads created) × 100Lead to Toured Rate = (4 ÷ 10) × 100 = 40%

Let’s say in January, a property had:

• 10 leads created during the month.

• All 10 eventually completed a tour (6 in January and 4 in Febrauary).

• Out of those 10:

◦ 4 leads submitted an application after touring:

▪ 2 applied in January

▪ 2 applied in February

◦ 6 leads toured but did not apply.

✅ These 4 applications are counted because the leads were created in January and toured before applying.

Toured to Applied Rate = (4 ÷ 10) × 100 = 40%

Let’s say at a property in January:

• 10 leads were created in January.

• Out of these:

◦ 10 leads submitted an application (application dates may vary).

◦ 5 of those leads signed a lease after applying:

▪ 3 signed in January

▪ 2 signed in February

◦ 5 leads applied but did not lease.

We include all 5 leased leads because they applied and leased after being created in January.

Applied to Leased Rate = (5 ÷ 10) × 100 = 50%

Let’s say at a property in January:

• 10 new leads were created — they submitted a form, signed up, or were added manually in January.

• Out of these 10 leads: ◦ 4 eventually scheduled a tour:

▪ 2 scheduled in January

▪ 2 scheduled in February

◦ 6 did not schedule any tour yet

Again, the scheduling can occur anytime. We only care that the leads were created in January.

Created to Scheduled Rate = (4 ÷ 10) × 100 = 40%

 For a property/PMC during the month of January:

• 150 people visited the property website from an ILS link (like Apartments.com) • 100 people found the website through Google Search.

• 30 people clicked on a paid social media ad and visited the site.

• 20 users opened the app directly to browse floor plans or availability.

Even though these interactions came from different sources, each unique user is only counted once, no matter how many times they visited.

Total Users = 150 + 100 + 30 + 20 = 300 unique users

(If someone visited multiple times from different channels, they are still counted as one user.) 

 For a property or PMC during the month of January:

• User A visits the property website on Monday and again on Wednesday.

• User B visits the website once on Friday.

Even though only two unique users visited the website, the total number of sessions is three, because each separate visit (regardless of user identity or repeated visits) counts as a new session.

Total Sessions = 2 (User A) + 1 (User B) = 3 sessions

Note: A session begins when a user starts interacting with the site and ends after 30 minutes of inactivity or when the day changes. 

 A property website receives 200 total visits during the month of January.

Out of those:

• 70 users stayed on the site for more than 10 seconds.

• 30 users viewed at least 2 pages.

• 20 users filled out a contact form or clicked to schedule a tour.

Some users may meet more than one criterion, but they are only counted once per visit if they meet any of the engagement thresholds.

So, the total number of visits that qualify as Engaged Sessions = 120 (70 + 30 + 20, with duplicates removed)

Engaged Sessions = 120

This indicates that 60% of visits showed real interest or interaction beyond a casual glance. 

 During the month of January:

• 10 unique users visited your property website or app.

• Across all their visits, they triggered a total of 50 engaged sessions — meaning each session lasted more than 10 seconds, had 2+ page views, or included a key interaction like a form fill or click.

Therefore Engaged Sessions per User is 50/10 = 5

This means, on average, each user engaged meaningfully 5 times during the reporting period — a strong indicator of interest and quality traffic. 

 For Example:

• If 400 sessions occur and 100 bounce, Bounce Rate = (100 ÷ 400) × 100 = 25%.

• If your Engagement Rate is 60%, then the Bounce Rate would be:

• Bounce Rate = 100% − 60% = 40% 

 Let’s say you’re tracking how long users spend actively engaging with your property website during their sessions—this includes activities like scrolling, clicking, or watching media. Sample Session Times:

You observe 3 user sessions with the following engagement durations:

• User A: 10 seconds

• User B: 20 seconds

• User C: 30 seconds

These times reflect how long users were actively interacting with the page, not just how long the page was open. Therefore Average Engagement Time is 60/3 = 20 seconds 

 For a property website, during a single session:

• One user visits the Homepage, which loads once.

• Then they click to view the About page, which also loads.

• After exploring the About page, they hit the browser's back button, returning to the Homepage, which loads a second time.

In this case:

• Homepage = 2 views

• About Page = 1 view

Total Views = 2 (Homepage) + 1 (About Page) = 3 Views

Even though it was just one user in one session, every time a page loads, it counts as a new view. 

 Let’s say you’re tracking user activity on your property website over a selected time period.

• User 1 views 5 pages (Homepage, Floor Plans, Amenities, Contact, and Gallery)

• User 2 views 3 pages (Homepage, Schedule Tour, Contact)

• User 3 views 4 pages (Homepage, Availability, Pricing, Apply Now)

• User 4 views 4 pages (Homepage, Neighborhood, Blog, FAQs)

Total Views = 5 (User 1) + 3 (User 2) + 4 (User 3) + 4 (User 4) = 16 Views Number of Users = 4 Views per User = Total Views / Total Users = 16 / 4 = 4 

 For a property website in the month of January:

• 20 users clicked the “Schedule a Tour” button

• 15 users submitted the contact form

• 10 users played a video

• 25 users clicked to view pricing

• 10 users clicked on the photo gallery

Even though some users may have performed more than one action, every interaction counts individually.

Total Events Triggered = 20 (tour clicks) + 15 (form submissions) + 10 (video plays) + 25 (pricing clicks) + 10 (gallery views) = 80 events

This means users triggered a total of 80 actions during the selected period. 

 In January, a property’s website tracked key actions taken by users.

Here's a breakdown of the activities:

1. Contact Form Submissions: ◦ 12 users submitted a contact form on the website. ◦

Each submission counts as a conversion because it’s a key action indicating interest.

2. Tour Scheduling: ◦ 8 users scheduled a tour through the website’s scheduling system.

◦ Since scheduling a tour is also a valuable action showing intent to visit, each of these counts as a conversion.

3. Lease Applications:

◦ 4 users submitted a lease application.

◦ Applying for a lease is a crucial step in the rental process, so each application is counted as a conversion.

Total Conversions Calculation:

• 12 contact form submissions = 12 conversions

• 8 tour schedules = 8 conversions

• 4 lease applications = 4 conversions

Total Conversions for January = 12 + 8 + 4 = 24 conversions. 

 Let’s say you manage a property with 150 rental units in total.

• 120 units are currently occupied by tenants. • The remaining 30 units are vacant.

To calculate the Occupancy Rate:

Occupancy Rate = (Occupied Units ÷ Total Units) × 100

Therefore, Occupancy Rate = (120 occupied units ÷ 150 total units) × 100 = 80%

This means the Occupancy Rate is 80%, indicating that 80% of the units are occupied. 

 Let’s say during the month of January, your team spent:

• $1,000 on Google Ads

• $500 on Facebook Ads

• $200 on ILS listing upgrades

Total Spend = $1,000 + $500 + $200 = $1,700 

 Let’s say during the month of January, a property ran marketing campaigns to drive tour bookings from potential renters. Ad Spend Breakdown:

• A total of $600 was spent across various digital channels:

◦ $300 on Google Ads targeting renters actively searching for apartments

◦ $200 on Facebook and Instagram ads promoting tour availability

◦ $100 on boosted listings via Internet Listing Services (ILS) Outcome:

• These efforts led to 30 unique leads scheduling their first tour through the website, ILS, or a Hyly.AI tour scheduler.

Therefore Cost per Scheduled Contact is 600/30 = $20

Note: This example considers the total spend aggregated across all vendors. 

 Let’s say in the month of January, a property spent money on marketing to drive tour bookings. Here’s the breakdown:

• $800 was spent on a combination of Google Ads, Facebook promotions, and ILS placements

• This resulted in 20 leads completing a first tour during that time Cost per Toured Contact is 800/20 = $40

Note: This example considers the total spend aggregated across all vendors. 

 Let’s say during the month of January, a property invested in marketing to drive applications. Here’s the breakdown:

• The property spent $1,000 across multiple marketing channels, including:

• Google Ads targeting renters actively searching

• Retargeting ads on Facebook and Instagram

• Sponsored placements on Internet Listing Services (ILS)

As a result, 50 unique leads submitted their first rental application during the same time period Cost per Applied Contact = $20 Note: This example considers the total spend aggregated across all vendors. 

 Let’s say for the month of January, a property focused on converting prospects all the way through to move-ins.

The total marketing spend was $2,000, allocated across:

• $1,000 on Google Search and Display Ads to capture high-intent renters

• $500 on social media ads (Facebook/Instagram) to retarget site visitors

• $300 for premium placements on Internet Listing Services (ILS)

• $200 on email nurturing campaigns and SMS reminders

Leasing Outcome: This campaign resulted in 10 unique leads successfully completing their first move-in during January.

Cost per Leased Contact = $200

Note: This example considers the total spend aggregated across all vendors. 

 Let’s say during the month of January, a property ran digital marketing campaigns to drive lead generation and tour bookings. Breakdown of Spend:

• $600 was spent on Google Ads, including:

◦ Search ads targeting "apartments near me"

◦ Display ads on rental-focused websites

• $400 was spent on Meta (Facebook & Instagram), including:

◦ Retargeting campaigns for website visitors

◦ Carousel ads showcasing floor plans and amenities.

These platforms were used to increase awareness, drive traffic, and capture new leads.

Total Ad Spend = $1,000

Note: This example considers the total spend aggregated across all vendors. 

 Let’s say during the month of January, a property ran a digital ad campaign across platforms like Google Display Network, Facebook, and Instagram.

Campaign Performance:

• The ad promoting the property’s new lease specials appeared in:

◦ Google Display ads on rental blogs and news sites

◦ Facebook feeds of targeted renters

◦ Instagram Stories featuring property highlights

As a result, the ad was displayed 12,000 times across all these platforms. Total Ad Impressions = 12,000 

 For a property’s digital ad campaign in January:

• The ad was displayed across various platforms like Google and Meta.

• 100 people clicked on the ad and were taken to a landing page with a contact form.

• Out of those, 25 users filled out the form with their name, email, and phone number.

Each form submission is counted as a conversion because it marks a key action taken by the user.

This means that 25 people completed a meaningful action after clicking the ad, showing the campaign’s ability to generate leads from ad traffic. 

 Let’s say during the month of January, a property ran a digital advertising campaign across platforms like Google Ads, Facebook, and Instagram to promote available units.

Campaign Objective: 

The goal was to drive potential renters to the property’s website, tour scheduler, or availability page. Ad Performance:

• The ad appeared across multiple channels with messaging like "Now Leasing — Tour Today!"

• As a result, 180 users clicked on the ad to learn more about the property, view floor plans, or schedule a tour 180

Ad Clicks=Number of Users Who Clicked the Ad=180 Total Ad Clicks = 180 

 A property runs an ad campaign. Ad Platforms:

Google & Meta (Facebook/Instagram)

Campaign Duration: 2 weeks Interactions Collected:

• Google Ads: 120 clicks, 45 video views

• Meta Ads: 80 likes, 30 shares, 25 comments, 65 “Apply Now” clicks

Total Ad Interactions = 120 + 45 + 80 + 30 + 25 + 65 = 365 

 Let’s say during the month of January, a property ran a digital advertising campaign across platforms like Google Search and Facebook Ads to drive traffic to its website.

Ad Spend Breakdown:

• $200 spent on Google Search Ads targeting renters searching for keywords like “apartments near me” or “1-bedroom in Dallas”

• $100 spent on Facebook Ads retargeting visitors who had previously viewed floor plans Click Performance:

• Across both platforms, the ads generated a total of 150 clicks to the property’s website or landing page

Cost Per Click = $2.00 

 For a property’s ad campaign in January:

• The ad was shown 1,000 times across Google and Meta (this is your total impressions).

• Out of those, 100 people clicked on the ad (this is your total clicks).

This means that 10% of users who saw your ad clicked on it, indicating how effective your ad was in generating engagement. 

 A property ran ads on Google and Facebook for one month to get more prospects.

• Total Ad Spend: $2,500

• Total Conversions: 40 (25 tour bookings + 15 applications)

Cost per Conversion Formula:

Cost per Conversion = Ad Spend ÷ Conversions = $2,500 ÷ 40 = $62.50

This means the property spent $62.50 on average to get each conversion, whether that was a tour or an application. 

 Let’s say a property ran ads on Google and Facebook to attract new renters.

Here’s how it played out:

• They spent: $1,000 on ads

• They got:

5 new leases from those ads

• Each lease brought in:

$1,000 in first-month rent

• Total revenue from leases: 5 × $1,000 = $5,000 ROAS = Revenue from Leases ÷ Ad Spend ROAS = $5,000 ÷ $1,000 = 5.0 

 Let’s say a property ran ads on Google and Facebook to increase visibility for a new lease-up. Here’s how it worked out:

• They spent: $250 on ads

• Their ads were shown: 50,000 times (impressions)

CPM Calculation:

CPM = (Cost ÷ Impressions) × 1,000

CPM = ($250 ÷ 50,000) × 1,000

CPM = 0.005 × 1,000 = $5

So the property paid $5 for every 1,000 ad views. This means they were able to reach 50,000 potential renters for just $250 — a cost-effective way to boost visibility during a critical leasing period. 

 Lets say a property ran a Facebook and a Google ad campaign to increase tour bookings.

Here’s what happened:

• Total Ad Interactions: 500

• Conversions (Tour Schedules): 75

Ad Conversion Rate Calculation:

Ad Conversion Rate = (75 ÷ 500) × 100 = 15%

This means 15% of people who interacted with the ad actually scheduled a tour—showing the campaign was doing a solid job of moving prospects forward in the leasing journey. 

A property launched a digital campaign across Google Search and Facebook Ads to drive new leases.

Here’s what happened during the month:

• Ad Spend: $800

• Conversions from Ads: 4 prospects signed leases after engaging with the ads

• Average First-Month Rent: $1,250

Ad Revenue Calculation:

Ad Revenue = 4 leases × $1,250 = $5,000

This means the property earned $5,000 in lease revenue directly tied to the digital ad campaign.

Understanding Ad Revenue helps the marketing team evaluate if their ad budget is truly driving signed leases—and how much value they’re getting from every dollar spent. 

If 80 ad-driven leads booked a tour within the period:

Ad Scheduled Contacts = 80 

If 50 out of 200 tours came from ad-sourced leads:

Ad Toured Contacts = 50 

If 35 out of 100 total applicants started from ads:

Ad Applied Contacts = 35 

If 20 leases out of 50 total leases originated from ad-driven leads:

Ad Leased Contacts = 20 

 If ad spend = $1,000 and 100 new contacts were created via ads:

Ad Cost per Created Contact = 1,000 ÷ 100 = $10 

 If ad spend = $2,000 and 80 ad-driven contacts scheduled a tour:

Ad Cost per Scheduled Contact = 2,000 ÷ 80 = $25 

If total ad spend = $3,000 and 60 tours came from ad leads:

Ad Cost per Toured Contact = 3,000 ÷ 60 = $50 

If total ad spend = $4,000 and 40 ad-attributed contacts submitted an application:

Ad Cost per Applied Contact = 4,000 ÷ 40 = $100 

 For a property/PMC during the month of January:

• 30 people filled out the contact form on ILS platforms.

• 10 people scheduled a tour through an ILS like Apartments.com

Each of these interactions shows rental interest and results in a new lead being recorded.

Total Leads = 30 + 10 = 40

Each unique person who showed interest through any ILS channel is an ILS Lead. 

 For a property during the month of June:

• 3 users clicked the property website link from Apartments.com

• 2 users clicked from Zillow

• 1 user clicked from Rent.com

• All of them landed on the property’s website

Even though they came from different ILS platforms, all 6 are counted as ILS Property Website Leads.

Total ILS Property Website Leads = 3 + 2 + 1 = 6

These leads are tracked as soon as the user clicks the website link from an ILS and lands on the property site. 

 For a property/PMC during the month of January:

• 5 people saw the listing on Apartments.com and clicked the “Call Now” button to speak with the leasing office.

• 3 users visited the property’s website, noted the phone number, and called to ask about availability.

• 2 leads clicked the click-to-call link from a paid Google ad on mobile.

• 1 user found the number through a Facebook ad and called to ask about pet policies.

• 1 person scanned a QR code on a flyer, which opened a contact card and called directly.

All 12 of these call interactions were tracked either via call tracking software or logged by the onsite team.

Phone Calls = 5 (ILS calls) + 3 (website calls) + 2 (ad calls) + 1 (Facebook call) + 1 (QR code call) = 12 total tracked calls during January.

So, Phone Calls = 12 — reflecting total interest shown via phone communication. 

 For a property/PMC during the month of January:

• 2 prospects clicked the "Contact Us" button on the property’s website and sent inquiries asking about floor plan availability.

• 1 user filled out a “Request Info” form and submitted it via email, asking for a brochure.

• 1 visitor used the email link embedded in a digital ad to reach out with questions about lease terms.

• 1 lead used the ILS platform's email feature (like Apartments.com) to message the leasing team directly.

• 1 person replied to an automated follow-up email sent from the property CRM and started a conversation.

All of these interactions were tracked through Hyly.AI or the Property Management System (PMS).

Emails = 2 (website contact) + 1 (info form) + 1 (ad email) + 1 (ILS email) + 1 (CRM reply) = 6 total email inquiries for January.

So, Emails = 6, reflecting the number of prospects who reached out via email across different channels. 

 Let’s say in June, a property is listed on multiple ILS platforms like Apartments.com and Zillow.

• On Apartments.com, 15 users click on the "Schedule a Tour" button.

• On Zillow, 10 users do the same. Out of these 25 total clicks:

• Only 8 users actually complete the tour scheduling process.

• The remaining 17 users exit or abandon the form midway.

However, the ILS platforms still record all 25 clicks as leads, because each unique click on the "Schedule a Tour" button is counted—regardless of whether the tour is actually scheduled.

Total ILS Tour Schedule Leads = 25 

 Let’s say in the month of June:

• Your property is listed on Apartments.com and Zillow.

• On Apartments.com, the listing appeared in search results 3,500 times.

• On Zillow, it appeared 2,000 times.

Even if no one clicks on the listing, each time it shows up on a user’s screen, it counts as an impression.

👉 Total ILS Impressions = 3,500 (Apartments.com) + 2,000 (Zillow) = 5,500 impressions

This means your property was seen 5,500 times across ILS platforms—showing high visibility, regardless of clicks or inquiries. 

 For a property/PMC during the month of January:

• Your listings appeared 1,800 times on Apartments.com when users searched for apartments in your area.

• They showed up 900 times on Zillow in relevant search results.

• They were also displayed 300 times on Rent.com during user searches.

Even if users didn’t click on the listing, each appearance in search results is counted.

Search Results = 1,800 + 900 + 300 = 3,000 total appearances in renter searches across ILS platforms. 

 Let’s say a property listing on Apartments.com includes:

• A photo gallery

• A video walkthrough

• A virtual tour Now imagine the following user activity during a week:

• User A views the photo gallery and the virtual tour → 2 media views

• User B watches the video walkthrough → 1 media view

• User C views all three: photos, video, and virtual tour → 3 media views

• User D views only the photos → 1 media view

• User E views the virtual tour twice → 2 media views Total Media Views = 2 + 1 + 3 + 1 + 2 = 9

This means the listing’s media content was viewed 9 times in total, regardless of how many unique users there were.

Each view of each media type is counted separately. 

 For a property website or app during the month of January:

• 50 users opened the interactive map to explore the location of the community.

• 30 users viewed the neighborhood map to check nearby amenities like cafes, gyms, or schools.

• 20 users used the unit map to browse available apartments in the building layout.

• 10 users accessed the map via a virtual tour that included clickable property locations.

Each time a user opens any version of the map (regardless of type), it is counted as one Property Map view. Total Property Map Views = 50 (community map) + 30 (neighborhood map) + 20 (unit map) + 10 (virtual tour map)= 110

This indicates that the property’s location and layout tools were viewed 110 times by users, helping them explore the area and make informed leasing decisions. 

 A property runs social media campaigns on Facebook and Instagram to showcase community updates, events, and new amenities in the month of January.

Here’s how it played out the month of January:

• Number of posts made: 12

• Likes on each post: ◦ Post 1: 40 likes ◦ Post 2: 55 likes ◦ Post 3: 30 likes ◦ … ◦ Post 12: 60 likes

• Post-level likes: Each number above shows how many people liked a specific post.

• Property-level likes: When you add all likes across the 12 posts, the total is 520 likes for the month.

This helps the property team understand how much engagement their social media content is driving overall—and which posts perform best. 

 Let’s say a property shared a photo of their renovated clubhouse on Instagram.

• On July 1st, the post had 60 likes

• On July 31st, the same post had 85 likes New Likes = 85 - 60 = 25

So, this post received 25 new likes over the past week — indicating continued engagement even after the initial post date. 

 A property actively posts photos, events, and leasing updates on their Facebook and Instagram pages.

Here’s how their audience has grown:

• Start of the month of January: 950 followers

• End of the month of January: 1,200 followers Follower Growth:

They gained 250 new followers in the month of January.

This increase shows that their content is reaching more people and building interest in the community. 

 Let’s say a property posted a video tour of their newly renovated clubhouse on Facebook and Instagram.

• As of Monday, their Instagram page had 2,500 followers.

• After five days of engagement and boosted promotion, when checked again on Friday — their follower count is now 2,620.

New Followers = 2,620 - 2,500 = 120

This means 120 new people started following their page after seeing and engaging with their post — signalling stronger interest and expanding their reach for future marketing. 

 Property actively shares updates on Facebook and Instagram, including events, new amenities, and leasing offers in the month of January.

Here’s how it played out for January:

• Number of posts made: 10

• Comments per post:

◦ Post 1: 8 comments

◦ Post 2: 5 comments

◦ Post 3: 12 comments ◦ … ◦

Post 10: 10 comments

Post-level comments: Each number above shows how many users engaged with a specific post by commenting on it.

Property-level comments: Adding all comments across the 10 posts gives a total of 95 comments for the month.

These comments help the leasing team understand what prospects are curious about—like pricing, amenities, pet policies, or availability. 

 Let’s say your property shared a video tour of a model unit on Instagram.

• When checked last Friday, the post had 22 comments.

• After a new round of promotions and story shares, you checked again this Friday — it now shows 37 comments.

New Comments = 37 - 22 = 15

That means 15 new people left a comment in the past week — possibly asking about rent, amenities, or tour availability — showing increased engagement with the post. 

 ORA Score Calculation Breakdown 1. Single Property – Single Month

This is the most basic case. Each property receives one ORA score per month from J Turner.

Example:

Property A's ORA score for May = 67.4 → That’s the score we show in reports for that property and month.

2. Single Property – Multiple Months

When you select one property across several months, we calculate the average of all monthly scores for that property.

Example:

Property A scores:

• March = 66.0 • April = 67.2 • May = 68.4 Average ORA = (66.0 + 67.2 + 68.4) / 3 = 67.2 3.

Multiple Properties – Single Month

If you're viewing multiple properties for the same month, we calculate the average of all those property scores.

Example:

• Property A = 67.4

• Property B = 69.0

• Property C = 65.6

Average ORA = (67.4 + 69.0 + 65.6) / 3 = 67.33 4.

Multiple Properties – Multiple Months

This is the most common scenario in the Halo dashboard.

We calculate the overall average across all selected properties and all selected months.

Example:

You select 3 properties across 2 months (April and May).

That’s 3 × 2 = 6 values total. ORA Scores:

• Property A: April = 66.0, May = 67.0

• Property B: April = 68.0, May = 69.0

• Property C: April = 65.0, May = 66.0

Total = 66 + 67 + 68 + 69 + 65 + 66 = 401 Average = 401 / 6 = 66.83

5. PMC ORA Score (Portfolio Level)

This is the average of all property scores across the selected months for a specific PMC.

Example:

If the PMC had an average ORA of:

• April = 67.47 • May = 67.45

Then the PMC ORA score = (67.47 + 67.45) / 2 = 67.46 

 Imagine your company manages 3 properties. ORA Scores:

• April:

◦ Property A: 67.2

◦ Property B: 68.0

◦ Property C: 66.5

• May:

◦ Property A: 67.5

◦ Property B: 68.3

◦ Property C: 66.7

You have 6 scores total.

Calculation:

Add all the scores: 67.2 + 68.0 + 66.5 + 67.5 + 68.3 + 66.7 = 404.2

Divide by the no. of values which is 6 in this case: 404.2 ÷ 6 = 67.37

Final PMC ORA Score = 67.37

This is your company’s average reputation score across all properties and months.

It helps you see how your portfolio is performing overall. 

 J Turner calculates this monthly by averaging ORA scores from all properties in their national database.

In Hyly.AI, we receive this score monthly via API. When viewing multiple months, we show the average across the selected months.

Example:

• April: 65.8

• May: 66.2

National ORA Avg. = (65.8 + 66.2) / 2 = 66.0 

Opiniion calculates each rating based on the live scores shown on the respective platforms.

For example, if a property has:

• Google: 4.2

• Yelp: 3.8

• Apartments.com: 4.1

• Facebook: 4.5

• ApartmentRatings.com: 3.9

Then each tile in Halo shows the exact same score as Opiniion does for that platform.

For Overall Public Rating, Opiniion typically averages the scores of the public review sites it tracks for that property.

So using the numbers above:

Overall Public Rating = (4.2 + 3.8 + 4.1 + 4.5 + 3.9) ÷ 5 = 4.1 

 A property manager oversees a 200-unit multifamily property in Phoenix and has been allocating $2,000/month to Google and Facebook ads.

This budget was set based on past habits, without considering upcoming leasing demand.

After implementing Yardi SmartDA, the system analyzes:

• 60-day lease exposure (10 units expected to be vacant)

• Historical ad performance

• Market average spend (~$2,800/month for similar properties) SmartDA recommends:

• Good Spend: $1,800

• Better Spend: $2,500

• Best Spend: $3,000

In this scenario, the Standard Current Spend of $2,000 acts as a baseline to assess whether the property is under-spending, on track, or needs to increase budget to meet leasing goals. 

 Let’s say a property is reviewing its ad budget:

• Google Ads: $900

• Facebook Ads: $650

• Instagram: $200

• Apartments.com: $700

• Retargeting: $250

Total Spend = $900 + $650 + $200 + $700 + $250 = $2,700

SmartDA recommends:

• Good: $2,000 • Better: $2,500

• Best: $3,000 Since their current spend is $2,700, you're above the “Better” tier — closer to “Best.”

This helps them to decide if their ad investment aligns with their leasing needs. 

 Imagine your community has 4 units becoming vacant in the next 60 days and a stable local market.

SmartDA analyzes historical performance and forecasts:

• Good Spend: $1,800

• Expected Clicks: 400

• Expected Prospects:

15 Choosing the Good tier helps you meet basic exposure with modest reach.

It's useful if your current occupancy is high and marketing pressure is low. 

 Let’s say a property has 6 upcoming vacancies and wants to lease quickly to maintain occupancy.

Based on local demand and historical ad performance, SmartDA recommends:

• Better Spend: $2,000

• Projected Clicks: 600

• Projected Prospects: 22

• Projected Leases: 4

This level of spend gives the property a stronger reach and higher-quality traffic than the Good Spend tier, helping it stay ahead in a competitive leasing environment without committing to the highest investment. 

 Imagine a property is opening a new building with 10 vacant units that must be filled within 30 days.

SmartDA provides:

• Best Spend: $2,500

• Projected Ad Clicks: 850

• Projected Prospects: 35

• Projected Leases: 6

In this case, the Best Spend option helps the leasing team go all-in on filling units quickly by maximizing ad reach across Google, Meta, and ILS platforms.

The return is expected in the form of higher conversion rates and faster occupancy gains. 

 A 250-unit property has 12 units expected to become vacant in the next 60 days.

Based on similar properties and historical data,

SmartDA forecasts the following:

• Average Cost Per Lead: $100

• Leads needed to fill 12 units (assuming 25% lease conversion): 48 leads

• Total spend required: 48 leads × $100 = $4,800

So, SmartDA might offer: • Good: $3,000 (fills basic demand, ~7–8 leases) • Better: $4,200 (meets target demand, ~10–11 leases) • Best: $5,000 (fills all 12 expected vacancies) These tiers are data-backed and reflect how different budget levels impact the ability to meet exposure. 

 Imagine there’s a 180-unit property in Dallas.

• SmartDA looks at 100+ similar properties in Dallas with 150–200 units.

• The average spend across these properties is calculated to be $2,800/month.

• Your current ad spend is $1,500/month.

• While SmartDA may recommend $3,200/month to meet your upcoming vacancy needs (Recommended Spend), the Standard Avg. Market Spend of $2,800/month shows you’re currently spending well below market norms.

This metric doesn’t tell you what you need to spend, but helps anchor expectations against what others like you are spending. 

 Imagine a 250-unit property in Phoenix, with 10 leases to fill next month.

• SmartDA groups similar properties in Phoenix that are facing similar demand levels—let's say 9–12 move-ins.

• It analyzes their actual ad spend and calculates an average of $3,600/month.

• This becomes the DemandX Avg. Market Spend. You’re currently spending $2,000/month.

• Compared to the Standard Avg. Market Spend of $2,800, you seem close.

• But compared to the DemandX Avg. Market Spend of $3,600, you’re underspending relative to your demand.

This tells you:

the market spends more when demand is high, and to keep pace with leasing goals, your budget may need to increase. 

 Let’s say a property is preparing for the next leasing cycle:

• Vacant Units: 4

• Upcoming Move-Outs: 3

• Total Leasing Opportunities: 7 units

• Average Rent per Unit: $1,400

Estimated Revenue Opportunity = 7 × $1,400 = $9,800

This $9,800 figure helps the marketing and leasing team understand:

• What they stand to gain in rent if they fill all upcoming vacancies

• The financial justification for ad spend and urgency for campaign launches

If SmartDA recommends a $2,000 ad budget, that’s just 20% of your potential gain—a worthwhile trade-off if the campaign helps close those leases. 

 Imagine a property is planning a two-week digital leasing campaign:

• Ad Spend Planned: $1,500

• Target Platforms: Google Display + Facebook

• Based on previous performance and similar campaigns, SmartDA predicts: ◦ Estimated Clicks = 450

This means the marketing team can expect approximately 450 prospects to click through the ads—visiting the property website, landing pages, or scheduling tools.

They can then estimate performance downstream:

• If 10% of clicks convert to scheduled tours → ~45 tours

• If 25% of those tours convert to leases → ~11 new leases

This kind of forecasting allows teams to plan for leasing activity and staffing. 

 Let’s say a property is running a $2,000 campaign across Google and Facebook. Based on:

• Previous conversion rates • Target market behavior

• Property's leasing urgency The platform predicts:

• Estimated Conversions = 40 These conversions could include:

• 25 scheduled tours

• 10 applications submitted

• 5 leases signed

This forecast lets the leasing team anticipate traffic and prepare resources—like having leasing agents ready or optimizing the tour calendar. 

 Let’s say a property is preparing a $2,000 ad campaign on Google and Facebook.

Based on:

• Previous campaigns

• The property's leasing urgency

• Seasonality and platform benchmarks The platform estimates:

• Forecasted Clicks = 1,500

These 1,500 clicks might come from:

• 1,000 on Google search ads

(from renters actively searching for apartments)

• 500 from Facebook or Instagram display ads

(from renters engaging visually)

This gives the marketing team a good sense of how much traffic they can expect to the website or landing page, even before the campaign begins. 

 Let’s say a property runs a campaign using Google Search and Facebook Ads with a budget of $2,000.

The DemandX engine reviews:

• Previous leasing data

• Similar campaigns in your market

• Vacancy urgency and historical lead quality

It forecasts:

• 1,500 clicks

• 150 prospects — users who will likely take leasing actions like:

◦ Scheduling a tour

◦ Calling the property

◦ Filling out a contact form

These 150 forecasted prospects are people who go beyond clicks and show real intent to lease.

This helps the leasing and marketing teams align expectations, staffing, and follow-up workflows. 

 Let’s say a property is launching a new leasing campaign.

• Campaign Budget:

$2,000

• Targeting:

Google Display Network + Meta (Facebook/Instagram)

• Property Demand: High — lease-up phase

• Ad Strategy: Focused on awareness and retargeting Using SmartDA/DemandX intelligence, the platform analyzes:

• Market competition

• Historical click-through rates (CTR) • CPM (Cost per 1,000 impressions) benchmarks

• Channel-specific delivery efficiency

It estimates:

• Forecasted Exposure = 250,000 impressions

This means the property’s ads are expected to be shown 250,000 times to qualified audiences in the area, creating top-of-funnel awareness for the new phase. 

Chat Total New Leads is calculated as:

Chat Total New Leads = COUNT

(Guest Card Submits where h_assist = Hayley Chat)

(Only new lead creation events assisted by Hayley Chat are included.) 

 Total Bot Traffic is calculated as:

Total Bot Traffic = COUNT(DISTINCT Hayley Chat Sessions)

Where a Hayley Chat Session is defined as:

• Grouped by contact_id if available, otherwise by session_id

• All events occur within 30 minutes of one another

• All events share the same source 

 Total After Hour Tours is calculated as:

Total After Hour Tours = SUM(Tours where scheduled time is after-hours AND h_assist = Hayley Chat)

(After-hours are defined as 6 PM EST to 9 AM EST by default.) 

 Total Messages is calculated as:

Total Messages = COUNT(DISTINCT message_id) (Includes both question and answer message types recorded in the selected time range.) 

 Total After Hour Messages is calculated as:

Total After Hour Messages = COUNT(DISTINCT message_id where message_time is between 6 PM EST and 9 AM EST) (Includes both question and answer message types.) 

 Hayley Mail Total Click Rate is calculated as:

Hayley Mail Total Click Rate = SUM(Unique Clicks) ÷ SUM(Sends) (Where SUM includes only campaigns under the Hayley Mail product category, and unique clicks count the first click after each send event.) 

 Mail Total After Hour Tours is calculated as:

Mail Total After Hour Tours = SUM(Tours where time is after-hours AND h_assist = Hayley Mail) (After-hours are defined as 6 PM EST to 9 AM EST by default.) 

 Mail Total Tours Booked is calculated as:

Mail Total Tours Booked = SUM(Schedules where h_assist = Hayley Mail) (Only tour schedules in the selected time range and assisted by Hayley Mail are included.) 

 1. Convert Drip sends into agent hours

Agent Hours (Drips) = (Total Drip Sends × 15 mins) ÷ 60

2. Normalize per month per property

Hours per Month per Property = Total Drip Hours ÷ #Months ÷ #Properties

3. Convert hours into AMPS Drips AMPS = Hours per Month per Property ÷ 160 (160 = standard full-time agent work hours per month.) 

 Drips Total Sends is calculated as:

Drips Total Sends = SUM(Sends across all Drip email campaigns) (Only email campaigns categorized under Drips are counted.) 

 Drips Total Click Rate is calculated as:

Drips Total Click Rate = SUM(Unique Clicks) ÷ SUM(Sends) (Where SUM includes only Drip email campaigns.) 

 Drips Total Open Rate is calculated as:

Drips Total Open Rate = SUM(Unique Opens) ÷ SUM(Sends) (Where SUM includes only Drip email campaigns.) 

 1. Convert Blast sends into agent hours Agent Hours (Blasts) = (Total Blast Sends × 5 mins) ÷ 60

2. Normalize per month per property Hours per Month per Property = Total Blast Hours ÷ #Months ÷ #Properties

3. Convert hours into AMPS Blasts AMPS = Hours per Month per Property ÷ 160 (160 = standard full-time agent work hours per month.) 

 Blasts Total Sends is calculated as:

Blasts Total Sends = SUM(Sends across all Blast email campaigns) (Only email campaigns categorized under Blasts are included.) 

 Blasts Total Click Rate is calculated as:

Blasts Total Click Rate = SUM(Unique Clicks) ÷ SUM(Sends) (Where SUM includes only Blast email campaigns.) 

Blasts Total Open Rate is calculated as:

Blasts Total Open Rate = SUM(Unique Opens) ÷ SUM(Sends) (Where SUM includes only Blast email campaigns.) 

 Events Total Sends is calculated as:

Events Total Sends = SUM(Sends across all Event email campaigns) (Only campaigns categorized under Events are included.) 

 Events RSVPs is calculated as:

Events RSVPs = SUM(added_guest + 1 for all records where RSVP Status = “rsvped”) (Each RSVP counts as 1, plus any additional guests submitted with the RSVP.) 

 Events Attendance is calculated as:

Events Attendance = SUM(added_guest + 1 for all records where RSVP Status = “rsvped” AND Attendance Status = 1)

(Each attendee record adds 1; additional guests recorded in added_guest are also included.) 

1. Convert Event email sends into agent hours

Agent Hours (Event Emails) = (Total Event Sends × 10 mins) ÷ 60

2. Convert Event creation tasks into agent hours

Agent Hours (Event Creation) = (Total Events × 15 mins) ÷ 60

3. Add both to get total Event hours

Total Event Hours = Agent Hours (Event Emails) + Agent Hours (Event Creation)

4. Normalize per month per property Hours per Month per Property = Total Event Hours ÷ #Months ÷ #Properties

5. Convert hours into AMPS Events AMPS = Hours per Month per Property ÷ 160

(160 = standard full-time agent hours per month.) 

 Events Total Click Rate is calculated as:

Events Total Click Rate = SUM(Unique Clicks) ÷ SUM(Sends) (Where the SUM includes only Event email campaigns.) 

 Events Total Open Rate is calculated as:

Events Total Open Rate = SUM(Unique Opens) ÷ SUM(Sends) (Where the SUM includes only Event email campaigns.) 

Total Scheduler Traffic is calculated as:

Total Scheduler Traffic = SUM(Visits where root_page_url (GA) OR in_page_url (Hyly landing page) = property’s scheduler link AND contact is known AND scheduler is Hyly’s) (Only Hyly Tour Scheduler properties and only known contact visits are counted.) 

 Total After Hour Tours Booked is calculated as:

Total After Hour Tours Booked = SUM(Tours Scheduled where time is after-hours AND h_assist = Hayley Chat, Hyly.AI Tour Scheduler and Hayley Mail)

(After-hours = any tour scheduled between 6 PM EST and 9 AM EST.) 

Total Touring Responders is calculated as:

Total Touring Responders = SUM(Sends across all campaigns filtered as touring responders)

(Only autoresponder campaigns explicitly marked as touring responders are included.) 

1. Total Prospect Toured

Total Prospect Toured = SUM(Tours Completed tied to schedules where channel = Prospect OR channel = API)

2. Total Hyly Toured

Total Hyly Toured = SUM(Tours Completed tied to schedules where h_stream = hyly AND channel = Prospect)

(Includes completed tours originally scheduled via Mail Bot, Chat Bot, and Hyly Scheduler.)

3. Total Toured Total Toured = SUM(Tours Completed tied to schedules where channel = Prospect OR channel = API OR channel = Agent)

(Completed tours from all three channels—Prospect, Agent, API—are included.) 

1. Total Scheduled Tours

Total Scheduled Tours = SUM(Schedules) (Counts all first-time schedules; excludes all reschedules.)

2. Total Prospect Scheduled Tours

Total Prospect Scheduled Tours = SUM(Schedules where channel = Prospect OR channel = API)

3. Total Hyly Schedules (one line)

Total Hyly Schedules = SUM(Schedules where channel = Prospect AND h_stream = hyly)

(Includes schedules completed via Hayley Mail Bot, Hayley Chat Bot, and Hyly Scheduler.) 

1. Convert tour scheduling into agent hours

Agent Hours (Tours Scheduled) = (Total Tours Scheduled × 20 mins) ÷ 60

(20 minutes represents the typical manual effort of calling, emailing, following up, and confirming a tour.)

2. Convert tour reminders into agent hours (one line)

Agent Hours (Tour Reminders) = (Total Tour Reminders × 15 mins) ÷ 60

3. Add both to get total tour hours

Total Tour Hours = Agent Hours (Tours Scheduled) + Agent Hours (Tour Reminders)

4. Normalize per month per property Hours per Month per Property = Total Tour Hours ÷ #Months ÷ #Properties

5. Convert hours into AMPS

Tour AMPS = Hours per Month per Property ÷ 160

(160 = standard full-time agent work hours per month.) 

 1. Convert sends into agent hours

Agent Hours Added (Alerts) = (Total Alert Sends × 15 mins) ÷ 60

2. Normalize per month per property

Hours Added per Month per Property = Total Alert Hours ÷ #Months ÷ #Properties

3. Convert hours into AMPS

Alerts AMPS = Hours per Month per Property ÷ 160

(160 = standard full-time agent work hours per month) 

Alerts Total Sends is calculated as:

Alerts Total Sends = SUM(Sends across all Alert campaigns)

(This includes all sends, even when the same campaign sends repeatedly to the same contact.) 

Alerts Total Click Rate is calculated as:

Alerts Total Click Rate = SUM(Unique Clicks) ÷ SUM(Sends)

(Where the SUM includes only campaigns under the Alerts product category, and unique clicks count the first click after each send event.) 

Alerts Total Open Rate is calculated as:

Alerts Total Open Rate = SUM(Unique Opens) ÷ SUM(Sends)

(Where the SUM includes only campaigns under the Alerts product category, and unique opens are counted as the first open after each send event.) 

AMPS is calculated in four steps:

1. Convert each product’s activity into “agent hours added”

• Drips: Agent Hours = (Total Sends × 15 mins) ÷ 60

• Blasts: Agent Hours = (Total Sends × 5 mins) ÷ 60

• Tours Scheduled: Agent Hours = (Total Tours × 20 mins) ÷ 60

• Tour Reminders: Agent Hours = (Total Reminders × 15 mins) ÷ 60

• Alerts: Agent Hours =  (Total Sends × 15 mins) ÷ 60

• Hayley (Guided Chat): Agent Hours = (Total Sessions × 5 mins + 20 mins per scheduled tour or application) ÷ 60

• Events – Email Sends: Agent Hours = (Total Sends × 10 mins) ÷ 60

• Events – Creation: Agent Hours = (Total Events × 15 mins) ÷ 60

• Refer-a-Friend: Agent Hours = (Total Sends × 10 mins) ÷ 60

• Hayley Mail: Agent Hours = (15 mins initial + 10 mins per response + 20 mins per scheduled tour/application) ÷ 60

2. Convert minutes to hours Hours Added = Total Minutes Added ÷ 60

3. Normalize per month per property Hours Added per Month per Property = Total Hours Added ÷ #Months ÷ #Properties

4. Convert to Agent Equivalents (AMPS) AMPS = Hours per Month per Property ÷ 160

(160 = standard full-time agent hours per month) 

 Total Sends is calculated as:

Total Sends = SUM(Sends across all Hayley products) 

Total Tours is calculated as:

Total Tours = Number of Tours Scheduled via Hayley Mail + Hayley Chat Bot + Hyly Scheduler

(In practice, all scheduled-tour events captured through these tools are summed.) 

Total Click Rate is calculated as:

Total Clicks Rate = SUM(Unique Clicks)/SUM(Sends) Where sum is across all products in hayley suite.

That is Blasts, Drips, Alerts, Events, Refere a friend, Hyley Mail (Agent AI)

Additionally for Alerts, and Hayley Mail since same campaign can be sent multiple times to same contact, the calculation of unique clicks is calculated as first click after each send event if exists. 

 Total Open Rate is calculated as:

Total Open Rate = SUM(Unique Opens) ÷ SUM(Sends) Where SUM includes all sends and unique opens from Blasts, Drips, Alerts, Events, Refer-a-Friend, and Hayley Mail. Special rule for Alerts & Hayley Mail:

If the same campaign sends multiple times to the same contact, each send can count one unique open—specifically the first open after that send.