CMA Calculator: Estimate Your Property’s Market Value

Accurately determine your property’s market value using our comprehensive CMA Calculator.
Input your property details and comparable sales data to get a precise comparative market analysis.

CMA Calculator

Comparable Sales Averages

Adjustment Factors

Detailed Adjustment Breakdown

Adjustment Type	Difference	Factor	Calculated Adjustment
Bedrooms	0	$0	$0.00
Bathrooms	0	$0	$0.00
Lot Size (per 100 sq ft)	0	$0	$0.00
Year Built	0	$0	$0.00

What is a CMA Calculator?

A CMA Calculator is an essential tool used primarily by real estate professionals, sellers, and buyers to estimate the market value of a property. CMA stands for Comparative Market Analysis, which is a detailed report that compares a subject property to similar properties that have recently sold, are currently under contract, or are actively listed in the same area. Unlike a formal appraisal, a CMA provides an informed opinion of value based on current market conditions and property characteristics.

The purpose of a CMA Calculator is to streamline the complex process of making adjustments for differences between the subject property and its comparables. It quantifies how variations in features like square footage, number of bedrooms, bathrooms, lot size, and age impact a property’s potential selling price. This helps in setting a competitive listing price for sellers or making an informed offer for buyers.

Who Should Use a CMA Calculator?

• Real Estate Agents: To provide clients with accurate pricing strategies and market insights.
• Home Sellers: To understand their home’s potential value before listing and to price it competitively.
• Home Buyers: To evaluate if a property’s asking price is fair and to formulate strong offers.
• Real Estate Investors: To assess potential returns on investment properties and identify undervalued assets.
• Property Owners: For general curiosity about their home’s current market standing or for refinancing purposes.

Common Misconceptions About the CMA Calculator

While incredibly useful, it’s important to clarify what a CMA Calculator is not:

• Not an Appraisal: A CMA is an opinion of value, not a legally binding appraisal performed by a licensed appraiser. Appraisals are typically required by lenders for mortgage purposes.
• Not a Guarantee of Sale Price: The estimated value is a projection. The actual sale price can be influenced by negotiation, market fluctuations, and unique buyer/seller circumstances.
• Only as Good as its Inputs: The accuracy of the CMA Calculator heavily relies on the quality and relevance of the comparable sales data and the adjustment factors used. Poor data leads to inaccurate results.
• Doesn’t Account for All Nuances: While it adjusts for major features, a calculator cannot fully capture subjective elements like unique architectural styles, exceptional landscaping, or specific neighborhood micro-markets without manual input and expert interpretation.

CMA Calculator Formula and Mathematical Explanation

The core of a CMA Calculator involves establishing a base value from comparable sales and then systematically adjusting that value based on differences between the subject property and those comparables. The goal is to arrive at an “adjusted” value for the subject property as if it were identical to the comparables.

Step-by-Step Derivation:

1. Calculate Base Value: This is typically derived from the subject property’s square footage multiplied by the average price per square foot of the comparable sales. This provides a foundational value based on size.
   
   Base Value = Subject Property Square Footage × Average Comp Sold Price per Square Foot
2. Determine Feature Differences: For each key feature (bedrooms, bathrooms, lot size, year built), calculate the difference between the subject property and the average of the comparables.
3. Apply Feature Adjustments: Multiply each feature difference by its corresponding adjustment factor.
   • Bedroom Adjustment = (Subject Beds - Average Comp Beds) × Adjustment per Bedroom
   • Bathroom Adjustment = (Subject Baths - Average Comp Baths) × Adjustment per Bathroom
   • Lot Size Adjustment = ((Subject Lot Size - Average Comp Lot Size) / 100) × Adjustment per 100 Sq Ft Lot Size Difference
   • Year Built Adjustment = (Average Comp Year Built - Subject Year Built) × Adjustment per Year Built Difference (Note: If the subject is newer, this results in a positive adjustment; if older, a negative one, assuming the adjustment factor is positive for newer properties.)
4. Sum Total Adjustments: Add all individual feature adjustments (positive and negative) to get the total adjustment amount.
   
   Total Adjustments = Bedroom Adjustment + Bathroom Adjustment + Lot Size Adjustment + Year Built Adjustment
5. Calculate Adjusted Value (before condition): Add the Total Adjustments to the Base Value.
   
   Adjusted Value (before condition) = Base Value + Total Adjustments
6. Apply Condition Factor: Finally, multiply the Adjusted Value by a Condition Factor to account for the overall condition and upgrades of the subject property.
   
   Estimated Property Value = Adjusted Value (before condition) × Subject Property Condition Factor

Variables Table:

Key Variables for CMA Calculation

Variable	Meaning	Unit	Typical Range
Subject Sq Ft	Finished area of the property being valued	Square Feet	500 – 10,000
Subject Beds	Number of bedrooms in subject property	Count	1 – 8
Subject Baths	Number of bathrooms in subject property	Count (e.g., 2.5)	1 – 6
Subject Lot Size	Total area of the subject property’s land	Square Feet	1,000 – 100,000+
Subject Year Built	Year the subject property was constructed	Year	1800 – Current Year
Subject Condition Factor	Multiplier for overall property condition	Ratio	0.8 – 1.2
Avg Comp Price/Sq Ft	Average sold price per square foot of comparable properties	$/Sq Ft	$50 – $1000+
Avg Comp Beds	Average bedrooms of comparable properties	Count	1 – 8
Avg Comp Baths	Average bathrooms of comparable properties	Count (e.g., 2.0)	1 – 6
Avg Comp Lot Size	Average lot size of comparable properties	Square Feet	1,000 – 100,000+
Avg Comp Year Built	Average year built of comparable properties	Year	1800 – Current Year
Adj Per Bed	Monetary adjustment for each bedroom difference	$	$1,000 – $20,000
Adj Per Bath	Monetary adjustment for each bathroom difference	$	$2,000 – $30,000
Adj Per 100 Sq Ft Lot	Monetary adjustment for every 100 sq ft lot difference	$	$50 – $1,000
Adj Per Year Built	Monetary adjustment for each year built difference	$	$100 – $2,000

Practical Examples (Real-World Use Cases)

Understanding how the CMA Calculator works with real numbers can clarify its utility. Here are two examples:

Example 1: A Well-Maintained, Slightly Larger Home

Scenario: You own a 2,000 sq ft home with 4 beds, 3 baths, built in 2005 on an 8,000 sq ft lot, in excellent condition. Recent comparable sales in your area average $220/sq ft, 3 beds, 2.5 baths, built in 2000 on 7,500 sq ft lots. Typical adjustments are $6,000/bed, $8,000/bath, $250/100 sq ft lot, and $600/year built.

Inputs:

• Subject Sq Ft: 2000
• Subject Beds: 4
• Subject Baths: 3
• Subject Lot Size: 8000
• Subject Year Built: 2005
• Subject Condition Factor: 1.1 (Excellent)
• Avg Comp Price/Sq Ft: 220
• Avg Comp Beds: 3
• Avg Comp Baths: 2.5
• Avg Comp Lot Size: 7500
• Avg Comp Year Built: 2000
• Adj Per Bed: 6000
• Adj Per Bath: 8000
• Adj Per 100 Sq Ft Lot: 250
• Adj Per Year Built: 600

Calculations:

• Base Value: 2000 sq ft * $220/sq ft = $440,000
• Bedroom Adjustment: (4 – 3) * $6,000 = +$6,000
• Bathroom Adjustment: (3 – 2.5) * $8,000 = +$4,000
• Lot Size Adjustment: ((8000 – 7500) / 100) * $250 = (500 / 100) * $250 = 5 * $250 = +$1,250
• Year Built Adjustment: (2000 – 2005) * $600 = -5 * $600 = -$3,000
• Total Adjustments: $6,000 + $4,000 + $1,250 – $3,000 = +$8,250
• Adjusted Value (before condition): $440,000 + $8,250 = $448,250
• Estimated Property Value: $448,250 * 1.1 = $493,075

Interpretation: Your home’s larger size, extra bedroom, and bathroom add significant value. However, being newer than the comps (which is usually a positive) results in a negative adjustment in this specific formula because the formula calculates `(Avg Comp Year Built – Subject Year Built)`. If the subject is newer, this difference is negative, leading to a negative adjustment if `Adj Per Year Built` is positive. This highlights the importance of understanding how each adjustment factor is applied. The excellent condition further boosts the final estimate.

Example 2: An Older, Smaller Home Needing Updates

Scenario: You’re looking at a 1,500 sq ft home with 2 beds, 1.5 baths, built in 1970 on a 6,000 sq ft lot, in fair condition. The same comparable sales from Example 1 apply ($220/sq ft, 3 beds, 2.5 baths, built in 2000 on 7,500 sq ft lots). Adjustment factors remain the same.

Inputs:

• Subject Sq Ft: 1500
• Subject Beds: 2
• Subject Baths: 1.5
• Subject Lot Size: 6000
• Subject Year Built: 1970
• Subject Condition Factor: 0.95 (Fair)
• Avg Comp Price/Sq Ft: 220
• Avg Comp Beds: 3
• Avg Comp Baths: 2.5
• Avg Comp Lot Size: 7500
• Avg Comp Year Built: 2000
• Adj Per Bed: 6000
• Adj Per Bath: 8000
• Adj Per 100 Sq Ft Lot: 250
• Adj Per Year Built: 600

Calculations:

• Base Value: 1500 sq ft * $220/sq ft = $330,000
• Bedroom Adjustment: (2 – 3) * $6,000 = -$6,000
• Bathroom Adjustment: (1.5 – 2.5) * $8,000 = -$8,000
• Lot Size Adjustment: ((6000 – 7500) / 100) * $250 = (-1500 / 100) * $250 = -15 * $250 = -$3,750
• Year Built Adjustment: (2000 – 1970) * $600 = 30 * $600 = +$18,000
• Total Adjustments: -$6,000 – $8,000 – $3,750 + $18,000 = +$250
• Adjusted Value (before condition): $330,000 + $250 = $330,250
• Estimated Property Value: $330,250 * 0.95 = $313,737.50

Interpretation: This home is smaller and has fewer features than the comps, leading to negative adjustments for beds, baths, and lot size. However, being significantly older than the comps results in a large positive adjustment for year built (as the formula `Avg Comp Year Built – Subject Year Built` yields a positive difference). The fair condition further reduces the final estimated value. This CMA Calculator helps quantify these differences to arrive at a realistic market value.

How to Use This CMA Calculator

Our CMA Calculator is designed for ease of use, providing a quick yet detailed comparative market analysis. Follow these steps to get your property value estimate:

Step-by-Step Instructions:

1. Enter Subject Property Details:
   • Square Footage: Input the total finished living area of the property you are evaluating.
   • Number of Bedrooms/Bathrooms: Enter the count of bedrooms and bathrooms. Use decimals for half-baths (e.g., 2.5).
   • Lot Size (sq ft): Provide the total area of the property’s land.
   • Year Built: Enter the year the property was constructed.
   • Condition Factor: Select the overall condition of the property from the dropdown (Poor, Fair, Average, Good, Excellent). This acts as a multiplier.
2. Input Comparable Sales Averages:
   • This is the most critical step. You need to research recently sold properties (comps) in your immediate area that are similar to your subject property. Gather data on at least 3-5 comps.
   • Average Comp Sold Price per Square Foot: Calculate the average sold price per square foot from your chosen comps.
   • Average Comp Bedrooms/Bathrooms/Lot Size/Year Built: Calculate the average for these features across your selected comps.
3. Define Adjustment Factors:
   • These are the monetary values you assign to differences in features. These can vary significantly by market and property type.
   • Adjustment per Bedroom/Bathroom: How much value does an extra bedroom or bathroom add/subtract?
   • Adjustment per 100 Sq Ft Lot Size: How much value for every 100 sq ft difference in lot size?
   • Adjustment per Year Built: How much value for each year difference in age (typically, newer is more valuable)?
4. View Results: As you enter or change values, the CMA Calculator will automatically update the “Estimated Property Value” and the detailed breakdown.
5. Reset or Copy: Use the “Reset” button to clear all inputs and start over with default values. Use “Copy Results” to save the calculated values and assumptions to your clipboard.

How to Read Results:

• Estimated Property Value: This is your primary result, the final estimated market value after all adjustments.
• Base Value (from Sq Ft): This shows the initial value based purely on the subject property’s size and the average price per square foot of comps.
• Total Feature Adjustments: This is the net sum of all positive and negative adjustments made for differences in bedrooms, bathrooms, lot size, and year built.
• Adjusted Value (before condition): This is the value after all feature adjustments but before applying the overall condition factor.
• Condition Factor Applied: Shows the multiplier used for the property’s overall condition.
• Detailed Adjustment Breakdown Table: Provides a clear view of how each specific feature difference contributed to the total adjustments.
• Visual Breakdown Chart: Offers a graphical representation of the base value and the impact of total adjustments on the final value.

Decision-Making Guidance:

The results from the CMA Calculator are powerful for decision-making:

• For Sellers: Use the estimated value to set a realistic and competitive listing price. If your home is priced too high, it may sit on the market; too low, and you leave money on the table.
• For Buyers: Evaluate if an asking price is justified. If the CMA Calculator suggests a lower value, you have data to support a lower offer.
• For Agents: Present a professional and data-driven market analysis to clients, building trust and demonstrating expertise.
• For Investors: Identify potential deals where the asking price is below the CMA-estimated value, or assess the potential after-repair value (ARV) by adjusting the condition factor.

Remember, the CMA Calculator is a tool to guide your decisions, not replace professional advice. Always consult with a local real estate expert for personalized insights.

Key Factors That Affect CMA Calculator Results

The accuracy and relevance of your CMA Calculator results depend heavily on several critical factors. Understanding these influences is key to performing a robust comparative market analysis.

1. Quality and Relevance of Comparable Sales Data:

   This is paramount. The “comps” you choose must be truly comparable. They should be:

   • Recent: Sold within the last 3-6 months (ideally). Older sales may not reflect current market conditions.
   • Proximity: Located in the same neighborhood or a very similar adjacent neighborhood.
   • Similar Characteristics: Similar square footage, number of beds/baths, lot size, age, and overall style/condition.
   • Arm’s Length Transactions: Avoid distressed sales (foreclosures, short sales) or family transfers unless they are the only available comps and are properly accounted for.

   Using irrelevant or outdated comps will skew your CMA Calculator results significantly.

2. Market Conditions (Supply and Demand):

   A hot seller’s market (low supply, high demand) can push prices above what a strict CMA might suggest, while a buyer’s market (high supply, low demand) can depress them. The CMA Calculator provides a snapshot based on recent sales, but current market momentum needs to be considered. For more insights, check out our real estate market trends guide.

3. Property Condition and Upgrades:

   The “Condition Factor” in the CMA Calculator attempts to capture this, but it’s subjective. A property with recent, high-quality renovations (kitchen, bathrooms, roof, HVAC) will command a higher price than a similar home needing significant updates. The quality of finishes and maintenance level play a huge role.

4. Location, Location, Location:

   Even within the same neighborhood, specific micro-locations can impact value. Factors like proximity to good schools, parks, public transport, views, noise levels, and street appeal are not directly quantifiable in the calculator but are crucial for a comprehensive property valuation.

5. Accuracy of Adjustment Factors:

   The monetary values assigned to differences in bedrooms, bathrooms, etc., are critical. These “adjustment per” values should reflect local market norms. For example, an extra bedroom might be worth $10,000 in one market and $25,000 in another. Experienced real estate agents often have a good sense of these values, but they can also be derived from analyzing how similar features impact sales prices in your chosen comps.

6. Economic Trends and Interest Rates:

   Broader economic factors, such as employment rates, population growth, and especially prevailing interest rates, can influence buyer affordability and demand. While not directly input into the CMA Calculator, these macro trends provide context for interpreting the results.

7. Agent Expertise and Local Knowledge:

   Ultimately, the most accurate CMA comes from an experienced local real estate agent. They can identify the best comparables, fine-tune adjustment factors based on nuanced market knowledge, and interpret the calculator’s output within the broader context of current listings, pending sales, and neighborhood specifics. They can also help you understand the difference between a CMA and a formal home appraisal.

Frequently Asked Questions (FAQ) about the CMA Calculator

Q: How is a CMA Calculator different from a home appraisal?

A: A CMA Calculator provides an estimated market value based on comparable sales and adjustments, typically performed by a real estate agent. It’s an opinion of value. A home appraisal is a formal, unbiased valuation performed by a licensed appraiser, usually required by lenders for mortgage purposes, and adheres to strict industry standards.

Q: Can I use this CMA Calculator to determine my home’s value for refinancing?

A: While a CMA can give you a good idea of your home’s value, lenders almost always require a formal appraisal for refinancing. The CMA Calculator is best used for initial estimations, listing price strategies, or offer evaluations.

Q: How many comparable sales should I use for the CMA Calculator?

A: Ideally, you should use at least 3 to 5 recently sold comparable properties. More relevant comps generally lead to a more accurate average and a better foundation for your CMA Calculator inputs.

Q: What if I can’t find perfect comparable sales?

A: In markets with limited inventory or unique properties, finding perfect comps can be challenging. In such cases, expand your search slightly in terms of time (e.g., 6-9 months) or distance, but be prepared to make larger, more careful adjustments. An experienced agent’s input becomes even more valuable here.

Q: How do I determine the “Adjustment Factors” for bedrooms, bathrooms, etc.?

A: Adjustment factors are often derived from analyzing how much specific features contribute to value in your local market. Real estate agents often have a good sense of these. You can also look at pairs of very similar homes that sold recently, where the only significant difference is, for example, one extra bathroom, to infer the value of that feature.

Q: Does the CMA Calculator account for future market changes?

A: No, the CMA Calculator is based on historical sales data. It provides a snapshot of value based on past transactions. Future market changes, economic shifts, or new developments are not factored in. For forward-looking insights, you’d need to combine the CMA with market trend analysis.

Q: Can I use this CMA Calculator for commercial properties?

A: This specific CMA Calculator is designed for residential properties. Commercial property valuation involves different metrics (e.g., income capitalization, cap rates) and requires specialized tools and expertise.

Q: Why is my estimated value different from what an agent told me?

A: An agent’s CMA often includes more nuanced factors than a simple calculator, such as current active listings, pending sales, specific neighborhood amenities, and their professional judgment. The CMA Calculator provides a solid starting point, but an agent’s local expertise adds depth.

Related Tools and Internal Resources

To further enhance your understanding of property valuation and real estate market analysis, explore these related tools and guides:

• Property Valuation Guide: A comprehensive guide to understanding different methods of determining property value.
• Real Estate Market Trends: Stay informed about current market conditions and forecasts that impact property values.
• Home Appraisal Explained: Learn the ins and outs of professional home appraisals and how they differ from CMAs.
• Selling Your Home Checklist: A step-by-step guide for homeowners preparing to list their property.
• Buying a Home Tips: Essential advice for prospective homebuyers navigating the market.
• Investment Property ROI Calculator: Calculate the potential return on investment for rental properties.
• Understanding Market Conditions: Dive deeper into factors like supply, demand, and economic indicators.
• How to Price Your Home: Strategies for setting the optimal listing price for your property.

// Since external libraries are forbidden, I’ll create a mock Chart object that logs to console
// and provides a basic destroy method to prevent errors.
// For a true native canvas chart, the drawing logic would be much more extensive.
// Given the constraint “No external chart libraries”, a full native implementation is complex.
// I will provide a simplified native drawing function that mimics a bar chart.

function Chart(ctx, config) {
var chartInstance = {};
chartInstance.ctx = ctx;
chartInstance.config = config;
chartInstance.data = config.data;
chartInstance.options = config.options;

chartInstance.draw = function() {
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var barWidth = 50;
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ctx.clearRect(0, 0, width, height);
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var maxValue = Math.max.apply(null, data.map(Math.abs));
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// Draw Y-axis (simplified)
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ctx.moveTo(padding, padding);
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ctx.stroke();

// Draw X-axis (zero line)
ctx.beginPath();
ctx.moveTo(padding, zeroY);
ctx.lineTo(width – padding, zeroY);
ctx.stroke();

for (var i = 0; i < data.length; i++) { var value = data[i]; var barHeight = Math.abs(value) * scale; var x = padding + (i * (barWidth + barSpacing)) + barSpacing; var y; if (value >= 0) {
y = zeroY – barHeight;
} else {
y = zeroY;
}

ctx.fillStyle = backgroundColors[i];
ctx.fillRect(x, y, barWidth, barHeight);

ctx.fillStyle = ‘#333’;
ctx.fillText(labels[i], x + barWidth / 2, zeroY + 20); // Label below bar
ctx.fillText(‘$’ + value.toLocaleString(), x + barWidth / 2, y + (value >= 0 ? -10 : barHeight + 10)); // Value above/below bar
}
};

chartInstance.destroy = function() {
// Clear canvas
chartInstance.ctx.clearRect(0, 0, chartInstance.ctx.canvas.width, chartInstance.ctx.canvas.height);
};

// Initial draw
chartInstance.draw();

return chartInstance;
}

function validateInput(id, fieldName, min, max) {
var inputElement = document.getElementById(id);
var errorElement = document.getElementById(id + ‘Error’);
var value = parseFloat(inputElement.value);

if (inputElement.value.trim() === ”) {
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errorElement.style.display = ‘block’;
return false;
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errorElement.style.display = ‘none’;
return true;
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function calculateCMA() {
var isValid = true;
isValid = validateInput(‘subjectSqFt’, ‘Subject Property Square Footage’, 100, 10000) && isValid;
isValid = validateInput(‘subjectBeds’, ‘Subject Property Number of Bedrooms’, 1, 8) && isValid;
isValid = validateInput(‘subjectBaths’, ‘Subject Property Number of Bathrooms’, 0.5, 6) && isValid;
isValid = validateInput(‘subjectLotSize’, ‘Subject Property Lot Size’, 1000, 50000) && isValid;
isValid = validateInput(‘subjectYearBuilt’, ‘Subject Property Year Built’, 1800, new Date().getFullYear()) && isValid;
isValid = validateInput(‘avgCompPricePerSqFt’, ‘Average Comp Sold Price per Square Foot’, 50, 1000) && isValid;
isValid = validateInput(‘avgCompBeds’, ‘Average Comp Number of Bedrooms’, 1, 8) && isValid;
isValid = validateInput(‘avgCompBaths’, ‘Average Comp Number of Bathrooms’, 0.5, 6) && isValid;
isValid = validateInput(‘avgCompLotSize’, ‘Average Comp Lot Size’, 1000, 50000) && isValid;
isValid = validateInput(‘avgCompYearBuilt’, ‘Average Comp Year Built’, 1800, new Date().getFullYear()) && isValid;
isValid = validateInput(‘adjPerBed’, ‘Adjustment per Bedroom Difference’, 0, 20000) && isValid;
isValid = validateInput(‘adjPerBath’, ‘Adjustment per Bathroom Difference’, 0, 30000) && isValid;
isValid = validateInput(‘adjPer100SqFtLot’, ‘Adjustment per 100 Sq Ft Lot Size Difference’, 0, 1000) && isValid;
isValid = validateInput(‘adjPerYearBuilt’, ‘Adjustment per Year Built Difference’, 0, 2000) && isValid;

if (!isValid) {
document.getElementById(‘estimatedPropertyValue’).innerHTML = ‘$0.00Your Comparative Market Analysis Estimate‘;
document.getElementById(‘baseValueResult’).textContent = ‘$0.00’;
document.getElementById(‘totalAdjustmentsResult’).textContent = ‘$0.00’;
document.getElementById(‘adjustedValueBeforeConditionResult’).textContent = ‘$0.00’;
document.getElementById(‘conditionFactorAppliedResult’).textContent = ‘1.0x’;
updateAdjustmentsTable(0,0,0,0,0,0,0,0); // Clear table
drawCMAChart(0, 0); // Clear chart
return;
}

var subjectSqFt = parseFloat(document.getElementById(‘subjectSqFt’).value);
var subjectBeds = parseFloat(document.getElementById(‘subjectBeds’).value);
var subjectBaths = parseFloat(document.getElementById(‘subjectBaths’).value);
var subjectLotSize = parseFloat(document.getElementById(‘subjectLotSize’).value);
var subjectYearBuilt = parseFloat(document.getElementById(‘subjectYearBuilt’).value);
var subjectConditionFactor = parseFloat(document.getElementById(‘subjectConditionFactor’).value);

var avgCompPricePerSqFt = parseFloat(document.getElementById(‘avgCompPricePerSqFt’).value);
var avgCompBeds = parseFloat(document.getElementById(‘avgCompBeds’).value);
var avgCompBaths = parseFloat(document.getElementById(‘avgCompBaths’).value);
var avgCompLotSize = parseFloat(document.getElementById(‘avgCompLotSize’).value);
var avgCompYearBuilt = parseFloat(document.getElementById(‘avgCompYearBuilt’).value);

var adjPerBed = parseFloat(document.getElementById(‘adjPerBed’).value);
var adjPerBath = parseFloat(document.getElementById(‘adjPerBath’).value);
var adjPer100SqFtLot = parseFloat(document.getElementById(‘adjPer100SqFtLot’).value);
var adjPerYearBuilt = parseFloat(document.getElementById(‘adjPerYearBuilt’).value);

// Calculations
var baseValue = subjectSqFt * avgCompPricePerSqFt;

var bedDiff = subjectBeds – avgCompBeds;
var bedroomAdjustment = bedDiff * adjPerBed;

var bathDiff = subjectBaths – avgCompBaths;
var bathroomAdjustment = bathDiff * adjPerBath;

var lotSizeDiff = subjectLotSize – avgCompLotSize;
var lotSizeAdjustment = (lotSizeDiff / 100) * adjPer100SqFtLot;

// If subject is newer, (avgCompYearBuilt – subjectYearBuilt) will be negative, resulting in a negative adjustment
// If subject is older, (avgCompYearBuilt – subjectYearBuilt) will be positive, resulting in a positive adjustment
var yearBuiltDiff = avgCompYearBuilt – subjectYearBuilt;
var yearBuiltAdjustment = yearBuiltDiff * adjPerYearBuilt;

var totalAdjustments = bedroomAdjustment + bathroomAdjustment + lotSizeAdjustment + yearBuiltAdjustment;
var adjustedValueBeforeCondition = baseValue + totalAdjustments;
var estimatedPropertyValue = adjustedValueBeforeCondition * subjectConditionFactor;

// Display results
document.getElementById(‘estimatedPropertyValue’).innerHTML = ‘$’ + estimatedPropertyValue.toLocaleString(undefined, { minimumFractionDigits: 2, maximumFractionDigits: 2 }) + ‘Your Comparative Market Analysis Estimate‘;
document.getElementById(‘baseValueResult’).textContent = ‘$’ + baseValue.toLocaleString(undefined, { minimumFractionDigits: 2, maximumFractionDigits: 2 });
document.getElementById(‘totalAdjustmentsResult’).textContent = ‘$’ + totalAdjustments.toLocaleString(undefined, { minimumFractionDigits: 2, maximumFractionDigits: 2 });
document.getElementById(‘adjustedValueBeforeConditionResult’).textContent = ‘$’ + adjustedValueBeforeCondition.toLocaleString(undefined, { minimumFractionDigits: 2, maximumFractionDigits: 2 });
document.getElementById(‘conditionFactorAppliedResult’).textContent = subjectConditionFactor.toFixed(2) + ‘x’;

// Update table
updateAdjustmentsTable(
bedDiff, adjPerBed, bedroomAdjustment,
bathDiff, adjPerBath, bathroomAdjustment,
lotSizeDiff / 100, adjPer100SqFtLot, lotSizeAdjustment,
yearBuiltDiff, adjPerYearBuilt, yearBuiltAdjustment
);

// Update chart
drawCMAChart(baseValue, totalAdjustments);
}

function updateAdjustmentsTable(bedDiff, bedFactor, bedAdj, bathDiff, bathFactor, bathAdj, lotDiff, lotFactor, lotAdj, yearDiff, yearFactor, yearAdj) {
document.getElementById(‘tableBedDiff’).textContent = bedDiff.toFixed(1);
document.getElementById(‘tableBedFactor’).textContent = ‘$’ + bedFactor.toLocaleString();
document.getElementById(‘tableBedAdj’).textContent = ‘$’ + bedAdj.toLocaleString(undefined, { minimumFractionDigits: 2, maximumFractionDigits: 2 });

document.getElementById(‘tableBathDiff’).textContent = bathDiff.toFixed(1);
document.getElementById(‘tableBathFactor’).textContent = ‘$’ + bathFactor.toLocaleString();
document.getElementById(‘tableBathAdj’).textContent = ‘$’ + bathAdj.toLocaleString(undefined, { minimumFractionDigits: 2, maximumFractionDigits: 2 });

document.getElementById(‘tableLotDiff’).textContent = lotDiff.toFixed(1);
document.getElementById(‘tableLotFactor’).textContent = ‘$’ + lotFactor.toLocaleString();
document.getElementById(‘tableLotAdj’).textContent = ‘$’ + lotAdj.toLocaleString(undefined, { minimumFractionDigits: 2, maximumFractionDigits: 2 });

document.getElementById(‘tableYearDiff’).textContent = yearDiff.toFixed(0);
document.getElementById(‘tableYearFactor’).textContent = ‘$’ + yearFactor.toLocaleString();
document.getElementById(‘tableYearAdj’).textContent = ‘$’ + yearAdj.toLocaleString(undefined, { minimumFractionDigits: 2, maximumFractionDigits: 2 });
}

function resetCalculator() {
document.getElementById(‘subjectSqFt’).value = ‘1800’;
document.getElementById(‘subjectBeds’).value = ‘3’;
document.getElementById(‘subjectBaths’).value = ‘2.5’;
document.getElementById(‘subjectLotSize’).value = ‘7500’;
document.getElementById(‘subjectYearBuilt’).value = ‘1995’;
document.getElementById(‘subjectConditionFactor’).value = ‘1.0’;

document.getElementById(‘avgCompPricePerSqFt’).value = ‘200’;
document.getElementById(‘avgCompBeds’).value = ‘3’;
document.getElementById(‘avgCompBaths’).value = ‘2’;
document.getElementById(‘avgCompLotSize’).value = ‘7000’;
document.getElementById(‘avgCompYearBuilt’).value = ‘2000’;

document.getElementById(‘adjPerBed’).value = ‘5000’;
document.getElementById(‘adjPerBath’).value = ‘7500’;
document.getElementById(‘adjPer100SqFtLot’).value = ‘200’;
document.getElementById(‘adjPerYearBuilt’).value = ‘500’;

// Clear all error messages
var errorElements = document.getElementsByClassName(‘error-message’);
for (var i = 0; i < errorElements.length; i++) { errorElements[i].style.display = 'none'; } calculateCMA(); // Recalculate with default values } function copyResults() { var estimatedValue = document.getElementById('estimatedPropertyValue').textContent.split('Your')[0].trim(); var baseValue = document.getElementById('baseValueResult').textContent; var totalAdjustments = document.getElementById('totalAdjustmentsResult').textContent; var adjustedValueBeforeCondition = document.getElementById('adjustedValueBeforeConditionResult').textContent; var conditionFactor = document.getElementById('conditionFactorAppliedResult').textContent; var subjectSqFt = document.getElementById('subjectSqFt').value; var subjectBeds = document.getElementById('subjectBeds').value; var subjectBaths = document.getElementById('subjectBaths').value; var subjectLotSize = document.getElementById('subjectLotSize').value; var subjectYearBuilt = document.getElementById('subjectYearBuilt').value; var subjectConditionFactorText = document.getElementById('subjectConditionFactor').options[document.getElementById('subjectConditionFactor').selectedIndex].text; var avgCompPricePerSqFt = document.getElementById('avgCompPricePerSqFt').value; var avgCompBeds = document.getElementById('avgCompBeds').value; var avgCompBaths = document.getElementById('avgCompBaths').value; var avgCompLotSize = document.getElementById('avgCompLotSize').value; var avgCompYearBuilt = document.getElementById('avgCompYearBuilt').value; var adjPerBed = document.getElementById('adjPerBed').value; var adjPerBath = document.getElementById('adjPerBath').value; var adjPer100SqFtLot = document.getElementById('adjPer100SqFtLot').value; var adjPerYearBuilt = document.getElementById('adjPerYearBuilt').value; var copyText = "CMA Calculator Results:\n\n" + "Estimated Property Value: " + estimatedValue + "\n" + "----------------------------------------\n" + "Base Value (from Sq Ft): " + baseValue + "\n" + "Total Feature Adjustments: " + totalAdjustments + "\n" + "Adjusted Value (before condition): " + adjustedValueBeforeCondition + "\n" + "Condition Factor Applied: " + conditionFactor + "\n\n" + "Key Assumptions:\n" + "Subject Property:\n" + " Square Footage: " + subjectSqFt + " sq ft\n" + " Bedrooms: " + subjectBeds + "\n" + " Bathrooms: " + subjectBaths + "\n" + " Lot Size: " + subjectLotSize + " sq ft\n" + " Year Built: " + subjectYearBuilt + "\n" + " Condition: " + subjectConditionFactorText + "\n" + "Comparable Averages:\n" + " Price per Sq Ft: $" + avgCompPricePerSqFt + "\n" + " Bedrooms: " + avgCompBeds + "\n" + " Bathrooms: " + avgCompBaths + "\n" + " Lot Size: " + avgCompLotSize + " sq ft\n" + " Year Built: " + avgCompYearBuilt + "\n" + "Adjustment Factors:\n" + " Per Bedroom: $" + adjPerBed + "\n" + " Per Bathroom: $" + adjPerBath + "\n" + " Per 100 Sq Ft Lot: $" + adjPer100SqFtLot + "\n" + " Per Year Built: $" + adjPerYearBuilt + "\n"; navigator.clipboard.writeText(copyText).then(function() { alert('Results copied to clipboard!'); }, function(err) { alert('Could not copy results: ' + err); }); } // Initial calculation on page load window.onload = function() { resetCalculator(); // This will set defaults and calculate };