264.TEMPLES OF INDIA DATASET ANALYSIS USING PROC PRINT | PROC CONTENTS | PROC SORT | PROC FREQ | PROC MEANS | PROC SQL | PROC TABULATE | PROC REPORT | PROC TRANSPOSE | PROC FORMAT | PROC RANK | PROC SGPLOT | MACROS

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220.ANALYZING GLOBAL MONEY LOAN TRENDS USING PROC CONTENTS | PROC PRINT | PROC MEANS | PROC FREQ | PROC SQL | PROC FORMAT | PROC REPORT | MACROS | OPTIONS | AND FUNCTIONS IN SAS

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ANALYZING GLOBAL MONEY LOAN TRENDS USING PROC CONTENTS | PROC PRINT | PROC MEANS | PROC FREQ | PROC SQL | PROC FORMAT | PROC REPORT | MACROS | OPTIONS | AND FUNCTIONS IN SAS

/*Creating a Money Loan Dataset with worldwide observations*/

Step 1: Setting Options and Creating the Loan Dataset

options nodate nonumber ls=120 ps=60 formdlim='*' msglevel=i;

%let today = %sysfunc(today(), date9.);


data money_loans;

    infile datalines dlm=',' dsd;

    length Loan_ID $8 Borrower_Name $25 Country $15 Currency $10 Loan_Type $15 Gender $6;

    input Loan_ID $ Borrower_Name $ Country $ Currency $ Loan_Amount Interest_Rate Loan_Term_Months Start_Date :date9. Loan_Type $ Gender $ Credit_Score Monthly_Income;

    format Loan_Amount dollar12.2 Interest_Rate percent8.2 Start_Date date9.;

    datalines;

L001,John Smith,USA,USD,50000,0.075,60,01JAN2021,Personal,Male,720,4000

L002,Ana Gomez,Mexico,MXN,300000,0.082,48,15FEB2022,Home,Female,680,3500

L003,Ravi Kumar,India,INR,700000,0.095,36,01MAR2021,Education,Male,650,25000

L004,Linda Chen,China,CNY,80000,0.07,24,20APR2023,Auto,Female,710,18000

L005,David Brown,UK,GBP,25000,0.065,12,05MAY2020,Business,Male,750,3000

L006,Noura Al-Fahad,UAE,AED,120000,0.06,36,10JUN2021,Personal,Female,730,20000

L007,Igor Petrov,Russia,RUB,500000,0.1,48,12JUL2022,Home,Male,660,30000

L008,Mei Tan,Singapore,SGD,15000,0.055,24,23AUG2021,Education,Female,770,5000

L009,Jose Silva,Brazil,BRL,40000,0.11,30,01SEP2020,Auto,Male,640,6000

L010,Maria Rossi,Italy,EUR,35000,0.068,60,14OCT2022,Business,Female,720,3500

L011,Tom Lee,South Korea,KRW,30000000,0.092,48,05NOV2021,Home,Male,700,4200000

L012,Fatima Zahra,Morocco,MAD,100000,0.085,36,22DEC2021,Personal,Female,710,12000

L013,Juan Carlos,Spain,EUR,28000,0.07,24,07JAN2023,Auto,Male,690,2800

L014,Emily Davis,Canada,CAD,22000,0.062,48,19FEB2022,Education,Female,760,4100

L015,Yuki Nakamura,Japan,JPY,2500000,0.058,60,11MAR2020,Business,Female,740,520000

L016,Ahmed Said,Egypt,EGP,150000,0.102,30,20APR2023,Home,Male,670,9000

;

run;

proc print;run;

Output:

Obs Loan_ID Borrower_Name Country Currency Loan_Type Gender Loan_Amount Interest_Rate Loan_Term_Months Start_Date Credit_Score Monthly_Income
1 L001 John Smith USA USD Personal Male $50,000.00 7.50% 60 01JAN2021 720 4000
2 L002 Ana Gomez Mexico MXN Home Female $300,000.00 8.20% 48 15FEB2022 680 3500
3 L003 Ravi Kumar India INR Education Male $700,000.00 9.50% 36 01MAR2021 650 25000
4 L004 Linda Chen China CNY Auto Female $80,000.00 7.00% 24 20APR2023 710 18000
5 L005 David Brown UK GBP Business Male $25,000.00 6.50% 12 05MAY2020 750 3000
6 L006 Noura Al-Fahad UAE AED Personal Female $120,000.00 6.00% 36 10JUN2021 730 20000
7 L007 Igor Petrov Russia RUB Home Male $500,000.00 10.00% 48 12JUL2022 660 30000
8 L008 Mei Tan Singapore SGD Education Female $15,000.00 5.50% 24 23AUG2021 770 5000
9 L009 Jose Silva Brazil BRL Auto Male $40,000.00 11.00% 30 01SEP2020 640 6000
10 L010 Maria Rossi Italy EUR Business Female $35,000.00 6.80% 60 14OCT2022 720 3500
11 L011 Tom Lee South Korea KRW Home Male $30000000.00 9.20% 48 05NOV2021 700 4200000
12 L012 Fatima Zahra Morocco MAD Personal Female $100,000.00 8.50% 36 22DEC2021 710 12000
13 L013 Juan Carlos Spain EUR Auto Male $28,000.00 7.00% 24 07JAN2023 690 2800
14 L014 Emily Davis Canada CAD Education Female $22,000.00 6.20% 48 19FEB2022 760 4100
15 L015 Yuki Nakamura Japan JPY Business Female $2500000.00 5.80% 60 11MAR2020 740 520000
16 L016 Ahmed Said Egypt EGP Home Male $150,000.00 10.20% 30 20APR2023 670 9000

Step 2: Summary using PROC CONTENTS and PROC PRINT

proc contents data=money_loans;

    title "Dataset Structure - Global Money Loan Records";

run;

Output:

Dataset Structure - Global Money Loan Records

The CONTENTS Procedure

Data Set Name WORK.MONEY_LOANS Observations 16
Member Type DATA Variables 12
Engine V9 Indexes 0
Created 14/09/2015 00:04:19 Observation Length 128
Last Modified 14/09/2015 00:04:19 Deleted Observations 0
Protection   Compressed NO
Data Set Type   Sorted NO
Label      
Data Representation WINDOWS_64    
Encoding wlatin1 Western (Windows)    


Engine/Host Dependent Information
Data Set Page Size 65536
Number of Data Set Pages 1
First Data Page 1
Max Obs per Page 511
Obs in First Data Page 16
Number of Data Set Repairs 0
ExtendObsCounter YES
Filename C:\Users\Lenovo\AppData\Local\Temp\SAS Temporary Files\_TD17164_DESKTOP-QFAA4KV_\money_loans.sas7bdat
Release Created 9.0401M2
Host Created X64_8HOME


Alphabetic List of Variables and Attributes
# Variable Type Len Format
2 Borrower_Name Char 25  
3 Country Char 15  
11 Credit_Score Num 8  
4 Currency Char 10  
6 Gender Char 6  
8 Interest_Rate Num 8 PERCENT8.2
7 Loan_Amount Num 8 DOLLAR12.2
1 Loan_ID Char 8  
9 Loan_Term_Months Num 8  
5 Loan_Type Char 15  
12 Monthly_Income Num 8  
10 Start_Date Num 8 DATE9.


proc print data=money_loans (obs=10);

    title "First 10 Observations - Loan Dataset";

run;

Output:

First 10 Observations - Loan Dataset

Obs Loan_ID Borrower_Name Country Currency Loan_Type Gender Loan_Amount Interest_Rate Loan_Term_Months Start_Date Credit_Score Monthly_Income
1 L001 John Smith USA USD Personal Male $50,000.00 7.50% 60 01JAN2021 720 4000
2 L002 Ana Gomez Mexico MXN Home Female $300,000.00 8.20% 48 15FEB2022 680 3500
3 L003 Ravi Kumar India INR Education Male $700,000.00 9.50% 36 01MAR2021 650 25000
4 L004 Linda Chen China CNY Auto Female $80,000.00 7.00% 24 20APR2023 710 18000
5 L005 David Brown UK GBP Business Male $25,000.00 6.50% 12 05MAY2020 750 3000
6 L006 Noura Al-Fahad UAE AED Personal Female $120,000.00 6.00% 36 10JUN2021 730 20000
7 L007 Igor Petrov Russia RUB Home Male $500,000.00 10.00% 48 12JUL2022 660 30000
8 L008 Mei Tan Singapore SGD Education Female $15,000.00 5.50% 24 23AUG2021 770 5000
9 L009 Jose Silva Brazil BRL Auto Male $40,000.00 11.00% 30 01SEP2020 640 6000
10 L010 Maria Rossi Italy EUR Business Female $35,000.00 6.80% 60 14OCT2022 720 3500

Step 3: Calculate Derived Fields with Functions

data money_loans_enhanced;

    set money_loans;

    End_Date = intnx("month", Start_Date, Loan_Term_Months);

    Loan_Duration_Years = round(Loan_Term_Months / 12, 0.1);

    Total_Interest = Loan_Amount * Interest_Rate * Loan_Duration_Years;

    Monthly_Installment = Loan_Amount / Loan_Term_Months + (Total_Interest / Loan_Term_Months);

    format End_Date date9. Total_Interest dollar12.2 Monthly_Installment dollar12.2;

run;

proc print data=money_loans_enhanced;

run;

Output:

Obs Loan_ID Borrower_Name Country Currency Loan_Type Gender Loan_Amount Interest_Rate Loan_Term_Months Start_Date Credit_Score Monthly_Income End_Date Loan_Duration_Years Total_Interest Monthly_Installment
1 L001 John Smith USA USD Personal Male $50,000.00 7.50% 60 01JAN2021 720 4000 01JAN2026 5.0 $18,750.00 $1,145.83
2 L002 Ana Gomez Mexico MXN Home Female $300,000.00 8.20% 48 15FEB2022 680 3500 01FEB2026 4.0 $98,400.00 $8,300.00
3 L003 Ravi Kumar India INR Education Male $700,000.00 9.50% 36 01MAR2021 650 25000 01MAR2024 3.0 $199,500.00 $24,986.11
4 L004 Linda Chen China CNY Auto Female $80,000.00 7.00% 24 20APR2023 710 18000 01APR2025 2.0 $11,200.00 $3,800.00
5 L005 David Brown UK GBP Business Male $25,000.00 6.50% 12 05MAY2020 750 3000 01MAY2021 1.0 $1,625.00 $2,218.75
6 L006 Noura Al-Fahad UAE AED Personal Female $120,000.00 6.00% 36 10JUN2021 730 20000 01JUN2024 3.0 $21,600.00 $3,933.33
7 L007 Igor Petrov Russia RUB Home Male $500,000.00 10.00% 48 12JUL2022 660 30000 01JUL2026 4.0 $200,000.00 $14,583.33
8 L008 Mei Tan Singapore SGD Education Female $15,000.00 5.50% 24 23AUG2021 770 5000 01AUG2023 2.0 $1,650.00 $693.75
9 L009 Jose Silva Brazil BRL Auto Male $40,000.00 11.00% 30 01SEP2020 640 6000 01MAR2023 2.5 $11,000.00 $1,700.00
10 L010 Maria Rossi Italy EUR Business Female $35,000.00 6.80% 60 14OCT2022 720 3500 01OCT2027 5.0 $11,900.00 $781.67
11 L011 Tom Lee South Korea KRW Home Male $30000000.00 9.20% 48 05NOV2021 700 4200000 01NOV2025 4.0 $11040000.00 $855,000.00
12 L012 Fatima Zahra Morocco MAD Personal Female $100,000.00 8.50% 36 22DEC2021 710 12000 01DEC2024 3.0 $25,500.00 $3,486.11
13 L013 Juan Carlos Spain EUR Auto Male $28,000.00 7.00% 24 07JAN2023 690 2800 01JAN2025 2.0 $3,920.00 $1,330.00
14 L014 Emily Davis Canada CAD Education Female $22,000.00 6.20% 48 19FEB2022 760 4100 01FEB2026 4.0 $5,456.00 $572.00
15 L015 Yuki Nakamura Japan JPY Business Female $2500000.00 5.80% 60 11MAR2020 740 520000 01MAR2025 5.0 $725,000.00 $53,750.00
16 L016 Ahmed Said Egypt EGP Home Male $150,000.00 10.20% 30 20APR2023 670 9000 01OCT2025 2.5 $38,250.00 $6,275.00

Step 4: Analyze with PROC MEANS and PROC FREQ

proc means data=money_loans_enhanced n mean median min max std maxdec=2;

    var Loan_Amount Interest_Rate Total_Interest Monthly_Income Monthly_Installment;

    title "Summary Statistics of Financial Fields";

run;

Output:

Summary Statistics of Financial Fields

The MEANS Procedure

Variable N Mean Median Minimum Maximum Std Dev
Loan_Amount
Interest_Rate
Total_Interest
Monthly_Income
Monthly_Installment
16
16
16
16
16
2166562.50
0.08
775859.44
304118.75
61409.74
90000.00
0.07
20175.00
7500.00
3643.06
15000.00
0.06
1625.00
2800.00
572.00
30000000.00
0.11
11040000.00
4200000.00
855000.00
7447759.64
0.02
2743097.42
1046683.58
212065.27

proc freq data=money_loans_enhanced;

    tables Country Gender Loan_Type Credit_Score;

    title "Frequency Distribution of Key Categorical Fields";

run;

Output:

Frequency Distribution of Key Categorical Fields

The FREQ Procedure

Country Frequency Percent Cumulative
Frequency		 Cumulative
Percent
Brazil 1 6.25 1 6.25
Canada 1 6.25 2 12.50
China 1 6.25 3 18.75
Egypt 1 6.25 4 25.00
India 1 6.25 5 31.25
Italy 1 6.25 6 37.50
Japan 1 6.25 7 43.75
Mexico 1 6.25 8 50.00
Morocco 1 6.25 9 56.25
Russia 1 6.25 10 62.50
Singapore 1 6.25 11 68.75
South Korea 1 6.25 12 75.00
Spain 1 6.25 13 81.25
UAE 1 6.25 14 87.50
UK 1 6.25 15 93.75
USA 1 6.25 16 100.00


Gender Frequency Percent Cumulative
Frequency		 Cumulative
Percent
Female 8 50.00 8 50.00
Male 8 50.00 16 100.00


Loan_Type Frequency Percent Cumulative
Frequency		 Cumulative
Percent
Auto 3 18.75 3 18.75
Business 3 18.75 6 37.50
Education 3 18.75 9 56.25
Home 4 25.00 13 81.25
Personal 3 18.75 16 100.00


Credit_Score Frequency Percent Cumulative
Frequency		 Cumulative
Percent
640 1 6.25 1 6.25
650 1 6.25 2 12.50
660 1 6.25 3 18.75
670 1 6.25 4 25.00
680 1 6.25 5 31.25
690 1 6.25 6 37.50
700 1 6.25 7 43.75
710 2 12.50 9 56.25
720 2 12.50 11 68.75
730 1 6.25 12 75.00
740 1 6.25 13 81.25
750 1 6.25 14 87.50
760 1 6.25 15 93.75
770 1 6.25 16 100.00


Step 5: Sorting and Filtering with PROC SQL

proc sql;

    create table top_borrowers as

    select Borrower_Name, Country, Loan_Amount, Monthly_Income

    from money_loans_enhanced

    where Credit_Score >= 720

    order by Loan_Amount desc;

    title "Top Borrowers with High Credit Score";

quit;

proc print;run;

Output:

Top Borrowers with High Credit Score

Obs Borrower_Name Country Loan_Amount Monthly_Income
1 Yuki Nakamura Japan $2500000.00 520000
2 Noura Al-Fahad UAE $120,000.00 20000
3 John Smith USA $50,000.00 4000
4 Maria Rossi Italy $35,000.00 3500
5 David Brown UK $25,000.00 3000
6 Emily Davis Canada $22,000.00 4100
7 Mei Tan Singapore $15,000.00 5000

Step 6: Macro for Country-Wise Loan Summary

%macro loan_by_country(ctry);

    proc sql;

        title "Loan Summary for &ctry";

        select 

            count(*) as Total_Loans,

            mean(Loan_Amount) as Avg_Loan format=dollar10.2,

            mean(Interest_Rate) as Avg_Rate format=percent6.2,

            sum(Total_Interest) as Total_Interest_Earned format=dollar15.2

        from money_loans_enhanced

        where Country = "&ctry";

    quit;

%mend;


%loan_by_country(India);

Output:

Loan Summary for India

Total_Loans Avg_Loan Avg_Rate Total_Interest_Earned
1 $700000.00 9.5% $199,500.00


%loan_by_country(USA);

Output:

Loan Summary for USA

Total_Loans Avg_Loan Avg_Rate Total_Interest_Earned
1 $50,000.00 7.5% $18,750.00


%loan_by_country(Brazil);

Output:

Loan Summary for Brazil

Total_Loans Avg_Loan Avg_Rate Total_Interest_Earned
1 $40,000.00 11% $11,000.00


Step 7: PROC FORMAT to Enhance Output

proc format;

    value $genderfmt

        "Male" = "M"

        "Female" = "F";


    value scoregrp

        low -< 650 = "Low"

        650 -< 700 = "Moderate"

        700 - high = "High";

run;


data money_loans_labeled;

    set money_loans_enhanced;

    Gender_Code = put(Gender, $genderfmt.);

    Score_Category = put(Credit_Score, scoregrp.);

run;

proc print;run;

Output:

Obs Loan_ID Borrower_Name Country Currency Loan_Type Gender Loan_Amount Interest_Rate Loan_Term_Months Start_Date Credit_Score Monthly_Income End_Date Loan_Duration_Years Total_Interest Monthly_Installment Gender_Code Score_Category
1 L001 John Smith USA USD Personal Male $50,000.00 7.50% 60 01JAN2021 720 4000 01JAN2026 5.0 $18,750.00 $1,145.83 M High
2 L002 Ana Gomez Mexico MXN Home Female $300,000.00 8.20% 48 15FEB2022 680 3500 01FEB2026 4.0 $98,400.00 $8,300.00 F Moderate
3 L003 Ravi Kumar India INR Education Male $700,000.00 9.50% 36 01MAR2021 650 25000 01MAR2024 3.0 $199,500.00 $24,986.11 M Moderate
4 L004 Linda Chen China CNY Auto Female $80,000.00 7.00% 24 20APR2023 710 18000 01APR2025 2.0 $11,200.00 $3,800.00 F High
5 L005 David Brown UK GBP Business Male $25,000.00 6.50% 12 05MAY2020 750 3000 01MAY2021 1.0 $1,625.00 $2,218.75 M High
6 L006 Noura Al-Fahad UAE AED Personal Female $120,000.00 6.00% 36 10JUN2021 730 20000 01JUN2024 3.0 $21,600.00 $3,933.33 F High
7 L007 Igor Petrov Russia RUB Home Male $500,000.00 10.00% 48 12JUL2022 660 30000 01JUL2026 4.0 $200,000.00 $14,583.33 M Moderate
8 L008 Mei Tan Singapore SGD Education Female $15,000.00 5.50% 24 23AUG2021 770 5000 01AUG2023 2.0 $1,650.00 $693.75 F High
9 L009 Jose Silva Brazil BRL Auto Male $40,000.00 11.00% 30 01SEP2020 640 6000 01MAR2023 2.5 $11,000.00 $1,700.00 M Low
10 L010 Maria Rossi Italy EUR Business Female $35,000.00 6.80% 60 14OCT2022 720 3500 01OCT2027 5.0 $11,900.00 $781.67 F High
11 L011 Tom Lee South Korea KRW Home Male $30000000.00 9.20% 48 05NOV2021 700 4200000 01NOV2025 4.0 $11040000.00 $855,000.00 M High
12 L012 Fatima Zahra Morocco MAD Personal Female $100,000.00 8.50% 36 22DEC2021 710 12000 01DEC2024 3.0 $25,500.00 $3,486.11 F High
13 L013 Juan Carlos Spain EUR Auto Male $28,000.00 7.00% 24 07JAN2023 690 2800 01JAN2025 2.0 $3,920.00 $1,330.00 M Moderate
14 L014 Emily Davis Canada CAD Education Female $22,000.00 6.20% 48 19FEB2022 760 4100 01FEB2026 4.0 $5,456.00 $572.00 F High
15 L015 Yuki Nakamura Japan JPY Business Female $2500000.00 5.80% 60 11MAR2020 740 520000 01MAR2025 5.0 $725,000.00 $53,750.00 F High
16 L016 Ahmed Said Egypt EGP Home Male $150,000.00 10.20% 30 20APR2023 670 9000 01OCT2025 2.5 $38,250.00 $6,275.00 M Moderate

proc freq data=money_loans_labeled;

    tables Score_Category;

    title "Credit Score Categories";

run;

Output:

Credit Score Categories

The FREQ Procedure

Score_Category Frequency Percent Cumulative
Frequency		 Cumulative
Percent
High 10 62.50 10 62.50
Low 1 6.25 11 68.75
Moderate 5 31.25 16 100.00


Step 8: Create a Report with PROC REPORT

proc report data=money_loans_enhanced nowd;

    column Loan_ID Borrower_Name Country Loan_Amount Loan_Term_Months Interest_Rate Computed_EMI;


    define Loan_ID / display;

    define Borrower_Name / display;

    define Country / display;

    define Loan_Amount / display format=dollar12.2;

    define Loan_Term_Months / display;

    define Interest_Rate / display format=percent6.2;

    define Computed_EMI / computed format=dollar10.2 'Recomputed EMI';


    compute Computed_EMI;

        Computed_EMI = Loan_Amount / Loan_Term_Months;

    endcomp;


    title "Loan Report with Recomputed EMI Using PROC REPORT";

run;

Output:

Loan Report with Recomputed EMI Using PROC REPORT

Loan_ID Borrower_Name Country Loan_Amount Loan_Term_Months Interest_Rate Recomputed EMI
L001 John Smith USA $50,000.00 60 7.5% $833.33
L002 Ana Gomez Mexico $300,000.00 48 8.2% $6,250.00
L003 Ravi Kumar India $700,000.00 36 9.5% $19,444.44
L004 Linda Chen China $80,000.00 24 7.0% $3,333.33
L005 David Brown UK $25,000.00 12 6.5% $2,083.33
L006 Noura Al-Fahad UAE $120,000.00 36 6.0% $3,333.33
L007 Igor Petrov Russia $500,000.00 48 10% $10,416.67
L008 Mei Tan Singapore $15,000.00 24 5.5% $625.00
L009 Jose Silva Brazil $40,000.00 30 11% $1,333.33
L010 Maria Rossi Italy $35,000.00 60 6.8% $583.33
L011 Tom Lee South Korea $30000000.00 48 9.2% $625000.00
L012 Fatima Zahra Morocco $100,000.00 36 8.5% $2,777.78
L013 Juan Carlos Spain $28,000.00 24 7.0% $1,166.67
L014 Emily Davis Canada $22,000.00 48 6.2% $458.33
L015 Yuki Nakamura Japan $2500000.00 60 5.8% $41,666.67
L016 Ahmed Said Egypt $150,000.00 30 10% $5,000.00


Step 9: Final Output Snapshot

proc print data=money_loans_labeled label;

    var Loan_ID Borrower_Name Country Loan_Amount Loan_Type Gender_Code Score_Category

        Monthly_Income Monthly_Installment;

    title "Final Enhanced Loan Dataset (Preview)";

run;

Output:
Final Enhanced Loan Dataset (Preview)

Obs Loan_ID Borrower_Name Country Loan_Amount Loan_Type Gender_Code Score_Category Monthly_Income Monthly_Installment
1 L001 John Smith USA $50,000.00 Personal M High 4000 $1,145.83
2 L002 Ana Gomez Mexico $300,000.00 Home F Moderate 3500 $8,300.00
3 L003 Ravi Kumar India $700,000.00 Education M Moderate 25000 $24,986.11
4 L004 Linda Chen China $80,000.00 Auto F High 18000 $3,800.00
5 L005 David Brown UK $25,000.00 Business M High 3000 $2,218.75
6 L006 Noura Al-Fahad UAE $120,000.00 Personal F High 20000 $3,933.33
7 L007 Igor Petrov Russia $500,000.00 Home M Moderate 30000 $14,583.33
8 L008 Mei Tan Singapore $15,000.00 Education F High 5000 $693.75
9 L009 Jose Silva Brazil $40,000.00 Auto M Low 6000 $1,700.00
10 L010 Maria Rossi Italy $35,000.00 Business F High 3500 $781.67
11 L011 Tom Lee South Korea $30000000.00 Home M High 4200000 $855,000.00
12 L012 Fatima Zahra Morocco $100,000.00 Personal F High 12000 $3,486.11
13 L013 Juan Carlos Spain $28,000.00 Auto M Moderate 2800 $1,330.00
14 L014 Emily Davis Canada $22,000.00 Education F High 4100 $572.00
15 L015 Yuki Nakamura Japan $2500000.00 Business F High 520000 $53,750.00
16 L016 Ahmed Said Egypt $150,000.00 Home M Moderate 9000 $6,275.00



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