# Property Scores

### **Property Score Formula**

Although properties are free to mint, we recognize some properties are more valuable or attractive to others, so we are assigning each Property a "Property Score".  We are leveraging properties found in mapping services like Google Maps as an initial method to determine "Property Scores".&#x20;



The following is the formula we will use to set the property score:  &#x20;

<figure><img src="https://3813000812-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FhtIbnBcOnoSwCftT0ex2%2Fuploads%2Fryq7quTLl3tUKe3SUxvT%2Fimage.png?alt=media&#x26;token=9706041b-0f7d-455c-9dc6-78f0abb21da3" alt=""><figcaption></figcaption></figure>

### Property Score Variables

Where:

* R = Google Maps Rating (e.g., customer review rating from 1 to 5)
* N = Number of Google Maps reviews
* C = Constant based on category importance
* w1,w2,w3 = Weights assigned to each component
  * _&#x77;_&#x31;=0.2 (for rating), _&#x77;_&#x32;=0.6 (for reviews), _&#x77;_&#x33;=0.2 (for category)
* λ = parameter to adjust sensitivity of number of reviews, 300 for all
* e = natural algorithm

#### Categories

#### High Traffic Tourist Attractions (1.0)

* These locations attract large numbers of visitors year-round and have high commercial potential.
* **Examples**: Iconic landmarks, major amusement parks, national parks, large museums, major convention centers, zoos, large stadiums, and airports.

#### Popular Shopping Malls and Entertainment Districts (0.8)

* These are popular destinations with significant foot traffic and commercial activity, but not as universally iconic as the highest category.
* **Examples**: Art galleries, city halls, department stores, hospitals, movie theaters, night clubs, large restaurants, shopping malls, universities, theaters, music venues, large casinos, and concert halls.

#### Suburban Areas with Good Amenities (0.6)

* These areas are attractive for families and long-term residents, with amenities that serve daily needs and local entertainment.
* **Examples**: Bakeries, banks, bars, book stores, cafes, car rentals, churches, clothing stores, dentists, doctors, florists, gas stations, gyms, hair salons, libraries, parks, schools, supermarkets, fitness centers, community centers, day care centers, pharmacies, small local restaurants, beauty salons, boutiques, pet stores, and veterinarians.

#### Rural Areas with Tourist Potential (0.4)

* These locations attract specific niches such as eco-tourists or are in rural settings with lower foot traffic but potential for tourism.
* **Examples**: Campgrounds, bed and breakfasts, farms, small museums, nature reserves, hiking trails, rural inns, local attractions, farm shops, eco-tourism sites, cottage rentals, agricultural cooperatives, and wineries.





### Property Score Examples

Take Tokyo Disneyland for example with 104,543 reviews and an average rating of 4.6



Given values:

* Rating (R): 4.6
* Number of reviews (N): 104,543
* Category score (C): 1.0&#x20;
* Lambda (λ): 300



<pre><code>Score = 10000 × (w1 ⋅ 4 + w2 ⋅ (1 - e^(-N / λ)) + w3 ⋅ C)

Calculate each term:

<strong>w1​⋅R = 0.2⋅4.6 = 0.92
</strong>
w2⋅(1−e−N/λ) = 0.6⋅(1−e−104543/300)≈ 0.6

w3⋅C = 0.2⋅1.0 = 0.2

Sum the terms:
Total Score = 10000 × (0.92 + 0.6 + 0.2) = 10000 × 1.72 = 17200
</code></pre>



Thus, the property score for Tokyo Disneyland is 17,200



### Property NFT Colors

* green = common&#x20;
* blue = uncommon&#x20;
* purple = epic&#x20;
* pink = legend&#x20;
* silver = city&#x20;
* gold = country

